{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "#coding:utf-8\n",
    "#导入warnings包，利用过滤器来实现忽略警告语句。\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import missingno as msno\n",
    "\n",
    "Train_data = df_train1 = pd.read_csv(r'data\\train1.txt',sep='\\t')\n",
    "Test_data = df_test = pd.read_csv(r'data\\test.txt',sep='\\t')\n",
    "df_submit = pd.read_csv(r'data\\submit.txt',sep='\\t',header=None)\n",
    "df_train2 = pd.read_csv(r'data\\train2.txt',sep='\\t',header=None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Train_data.head().append(Train_data.tail())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(30000, 36)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Train_data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>cardid</th>\n",
       "      <th>tradeTime</th>\n",
       "      <th>brand</th>\n",
       "      <th>serial</th>\n",
       "      <th>model</th>\n",
       "      <th>mileage</th>\n",
       "      <th>color</th>\n",
       "      <th>cityId</th>\n",
       "      <th>carCode</th>\n",
       "      <th>transferCount</th>\n",
       "      <th>...</th>\n",
       "      <th>v7</th>\n",
       "      <th>v8</th>\n",
       "      <th>v9</th>\n",
       "      <th>v10</th>\n",
       "      <th>v11</th>\n",
       "      <th>v12</th>\n",
       "      <th>v13</th>\n",
       "      <th>v14</th>\n",
       "      <th>v15</th>\n",
       "      <th>price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3</td>\n",
       "      <td>2021-09-26</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>6.64</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1+2</td>\n",
       "      <td>4878*1925*1734</td>\n",
       "      <td>201710.0</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4</td>\n",
       "      <td>2021-08-14</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>8.04</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>2018-06-14</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1+2</td>\n",
       "      <td>3723*1683*1407</td>\n",
       "      <td>201010.0</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>8</td>\n",
       "      <td>2021-10-09</td>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "      <td>10.19</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4415*1674*1415</td>\n",
       "      <td>201003.0</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>9</td>\n",
       "      <td>2021-09-30</td>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "      <td>2.27</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1+2</td>\n",
       "      <td>4649*1830*1705</td>\n",
       "      <td>201907.0</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>11</td>\n",
       "      <td>2021-08-09</td>\n",
       "      <td>8</td>\n",
       "      <td>11</td>\n",
       "      <td>11</td>\n",
       "      <td>7.03</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1+2</td>\n",
       "      <td>4933*1836*1469</td>\n",
       "      <td>201810.0</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4995</th>\n",
       "      <td>55510</td>\n",
       "      <td>2021-10-28</td>\n",
       "      <td>12</td>\n",
       "      <td>275</td>\n",
       "      <td>1023</td>\n",
       "      <td>8.53</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>2021-08-14</td>\n",
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       "      <td>4845*1805*1695</td>\n",
       "      <td>201702.0</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4996</th>\n",
       "      <td>1768</td>\n",
       "      <td>2021-08-23</td>\n",
       "      <td>17</td>\n",
       "      <td>480</td>\n",
       "      <td>1400</td>\n",
       "      <td>11.11</td>\n",
       "      <td>1</td>\n",
       "      <td>19</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3+2</td>\n",
       "      <td>4740*1770*1480</td>\n",
       "      <td>201409.0</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4997</th>\n",
       "      <td>20723</td>\n",
       "      <td>2021-09-03</td>\n",
       "      <td>7</td>\n",
       "      <td>166</td>\n",
       "      <td>1056</td>\n",
       "      <td>11.02</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>2015-10-14</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1</td>\n",
       "      <td>4850*1795*1475</td>\n",
       "      <td>201011.0</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4998</th>\n",
       "      <td>15625</td>\n",
       "      <td>2021-10-29</td>\n",
       "      <td>25</td>\n",
       "      <td>235</td>\n",
       "      <td>371</td>\n",
       "      <td>6.21</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1</td>\n",
       "      <td>4100*1740*1635</td>\n",
       "      <td>201604.0</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4999</th>\n",
       "      <td>51844</td>\n",
       "      <td>2021-09-05</td>\n",
       "      <td>8</td>\n",
       "      <td>11</td>\n",
       "      <td>9459</td>\n",
       "      <td>8.50</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>2021-08-27</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1+2</td>\n",
       "      <td>4872*1834*1484</td>\n",
       "      <td>201601.0</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10 rows × 36 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      cardid   tradeTime  brand  serial  model  mileage  color  cityId  \\\n",
       "0          3  2021-09-26      3       3      3     6.64      2       3   \n",
       "1          4  2021-08-14      4       4      4     8.04      3       1   \n",
       "2          8  2021-10-09      8       8      8    10.19      5       1   \n",
       "3          9  2021-09-30      9       9      9     2.27      2       2   \n",
       "4         11  2021-08-09      8      11     11     7.03      2       3   \n",
       "4995   55510  2021-10-28     12     275   1023     8.53      1       1   \n",
       "4996    1768  2021-08-23     17     480   1400    11.11      1      19   \n",
       "4997   20723  2021-09-03      7     166   1056    11.02      2       1   \n",
       "4998   15625  2021-10-29     25     235    371     6.21      4       5   \n",
       "4999   51844  2021-09-05      8      11   9459     8.50      2       1   \n",
       "\n",
       "      carCode  transferCount  ...          v7   v8   v9  v10  v11  \\\n",
       "0           1              0  ...         NaN  2.0  5.0  2.0  1+2   \n",
       "1           2              2  ...  2018-06-14  1.0  3.0  2.0  1+2   \n",
       "2           2              0  ...         NaN  1.0  4.0  3.0  NaN   \n",
       "3           4              0  ...         NaN  2.0  5.0  NaN  1+2   \n",
       "4           1              0  ...         NaN  2.0  4.0  NaN  1+2   \n",
       "4995        1              1  ...  2021-08-14  1.0  5.0  2.0  1+2   \n",
       "4996        2              0  ...         NaN  NaN  NaN  NaN  3+2   \n",
       "4997        2              1  ...  2015-10-14  1.0  4.0  3.0    1   \n",
       "4998        1              0  ...         NaN  1.0  5.0  2.0    1   \n",
       "4999        1              1  ...  2021-08-27  2.0  4.0  3.0  1+2   \n",
       "\n",
       "                 v12       v13  v14  v15  price  \n",
       "0     4878*1925*1734  201710.0    1  NaN    NaN  \n",
       "1     3723*1683*1407  201010.0    2  NaN    NaN  \n",
       "2     4415*1674*1415  201003.0    1  NaN    NaN  \n",
       "3     4649*1830*1705  201907.0    2  NaN    NaN  \n",
       "4     4933*1836*1469  201810.0    1  NaN    NaN  \n",
       "4995  4845*1805*1695  201702.0    1  NaN    NaN  \n",
       "4996  4740*1770*1480  201409.0    1  NaN    NaN  \n",
       "4997  4850*1795*1475  201011.0    1  NaN    NaN  \n",
       "4998  4100*1740*1635  201604.0    2  NaN    NaN  \n",
       "4999  4872*1834*1484  201601.0    2  NaN    NaN  \n",
       "\n",
       "[10 rows x 36 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Test_data.head().append(Test_data.tail())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(5000, 36)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Test_data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>cardid</th>\n",
       "      <th>brand</th>\n",
       "      <th>serial</th>\n",
       "      <th>model</th>\n",
       "      <th>mileage</th>\n",
       "      <th>color</th>\n",
       "      <th>cityId</th>\n",
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       "      <th>transferCount</th>\n",
       "      <th>seatings</th>\n",
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       "      <th>v3</th>\n",
       "      <th>v4</th>\n",
       "      <th>v5</th>\n",
       "      <th>v6</th>\n",
       "      <th>v8</th>\n",
       "      <th>v9</th>\n",
       "      <th>v10</th>\n",
       "      <th>v13</th>\n",
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       "      <th>price</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>30000.000000</td>\n",
       "      <td>30000.000000</td>\n",
       "      <td>30000.000000</td>\n",
       "      <td>30000.000000</td>\n",
       "      <td>30000.000000</td>\n",
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       "      <td>29991.000000</td>\n",
       "      <td>30000.000000</td>\n",
       "      <td>30000.000000</td>\n",
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       "      <td>30000.000000</td>\n",
       "      <td>17892.000000</td>\n",
       "      <td>30000.000000</td>\n",
       "      <td>30000.000000</td>\n",
       "      <td>26225.000000</td>\n",
       "      <td>26256.000000</td>\n",
       "      <td>23759.000000</td>\n",
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       "      <td>30000.000000</td>\n",
       "      <td>30000.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>37099.253567</td>\n",
       "      <td>19.228300</td>\n",
       "      <td>237.707733</td>\n",
       "      <td>5312.047767</td>\n",
       "      <td>7.144473</td>\n",
       "      <td>2.786700</td>\n",
       "      <td>7.683000</td>\n",
       "      <td>1.612184</td>\n",
       "      <td>0.510900</td>\n",
       "      <td>5.110000</td>\n",
       "      <td>...</td>\n",
       "      <td>2.031067</td>\n",
       "      <td>215.428683</td>\n",
       "      <td>26.313867</td>\n",
       "      <td>2.299467</td>\n",
       "      <td>1.582307</td>\n",
       "      <td>4.418761</td>\n",
       "      <td>2.507934</td>\n",
       "      <td>201449.680631</td>\n",
       "      <td>1.398133</td>\n",
       "      <td>18.062224</td>\n",
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       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>21423.562842</td>\n",
       "      <td>18.009168</td>\n",
       "      <td>233.329093</td>\n",
       "      <td>4055.789248</td>\n",
       "      <td>4.383048</td>\n",
       "      <td>2.037599</td>\n",
       "      <td>9.294335</td>\n",
       "      <td>0.809380</td>\n",
       "      <td>0.796347</td>\n",
       "      <td>0.671204</td>\n",
       "      <td>...</td>\n",
       "      <td>0.424548</td>\n",
       "      <td>196.882864</td>\n",
       "      <td>25.557234</td>\n",
       "      <td>1.718687</td>\n",
       "      <td>0.767380</td>\n",
       "      <td>0.614021</td>\n",
       "      <td>0.500200</td>\n",
       "      <td>282.230336</td>\n",
       "      <td>0.496486</td>\n",
       "      <td>629.444049</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.010000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
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       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>200606.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.050000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>18622.750000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>76.000000</td>\n",
       "      <td>1171.000000</td>\n",
       "      <td>3.907500</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>68.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>201211.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>6.100000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>36951.500000</td>\n",
       "      <td>13.000000</td>\n",
       "      <td>162.000000</td>\n",
       "      <td>4920.500000</td>\n",
       "      <td>6.540000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>156.000000</td>\n",
       "      <td>19.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>201504.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>10.479900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>55728.500000</td>\n",
       "      <td>23.000000</td>\n",
       "      <td>312.000000</td>\n",
       "      <td>8976.000000</td>\n",
       "      <td>9.540000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>14.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>310.000000</td>\n",
       "      <td>34.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>201702.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>18.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>74156.000000</td>\n",
       "      <td>133.000000</td>\n",
       "      <td>1248.000000</td>\n",
       "      <td>12735.000000</td>\n",
       "      <td>44.740000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>93.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>1037.000000</td>\n",
       "      <td>177.000000</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>202105.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>109000.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 29 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             cardid         brand        serial         model       mileage  \\\n",
       "count  30000.000000  30000.000000  30000.000000  30000.000000  30000.000000   \n",
       "mean   37099.253567     19.228300    237.707733   5312.047767      7.144473   \n",
       "std    21423.562842     18.009168    233.329093   4055.789248      4.383048   \n",
       "min        1.000000      1.000000      1.000000      1.000000      0.010000   \n",
       "25%    18622.750000      8.000000     76.000000   1171.000000      3.907500   \n",
       "50%    36951.500000     13.000000    162.000000   4920.500000      6.540000   \n",
       "75%    55728.500000     23.000000    312.000000   8976.000000      9.540000   \n",
       "max    74156.000000    133.000000   1248.000000  12735.000000     44.740000   \n",
       "\n",
       "              color        cityId       carCode  transferCount      seatings  \\\n",
       "count  30000.000000  30000.000000  29991.000000   30000.000000  30000.000000   \n",
       "mean       2.786700      7.683000      1.612184       0.510900      5.110000   \n",
       "std        2.037599      9.294335      0.809380       0.796347      0.671204   \n",
       "min        1.000000      1.000000      1.000000       0.000000      2.000000   \n",
       "25%        1.000000      1.000000      1.000000       0.000000      5.000000   \n",
       "50%        2.000000      4.000000      1.000000       0.000000      5.000000   \n",
       "75%        5.000000     14.000000      2.000000       1.000000      5.000000   \n",
       "max       10.000000     93.000000      6.000000      10.000000      9.000000   \n",
       "\n",
       "       ...            v3            v4            v5            v6  \\\n",
       "count  ...  30000.000000  17892.000000  30000.000000  30000.000000   \n",
       "mean   ...      2.031067    215.428683     26.313867      2.299467   \n",
       "std    ...      0.424548    196.882864     25.557234      1.718687   \n",
       "min    ...      1.000000      1.000000      1.000000      1.000000   \n",
       "25%    ...      2.000000     68.000000      9.000000      1.000000   \n",
       "50%    ...      2.000000    156.000000     19.000000      2.000000   \n",
       "75%    ...      2.000000    310.000000     34.000000      2.000000   \n",
       "max    ...     11.000000   1037.000000    177.000000     15.000000   \n",
       "\n",
       "                 v8            v9           v10            v13           v14  \\\n",
       "count  26225.000000  26256.000000  23759.000000   28381.000000  30000.000000   \n",
       "mean       1.582307      4.418761      2.507934  201449.680631      1.398133   \n",
       "std        0.767380      0.614021      0.500200     282.230336      0.496486   \n",
       "min        1.000000      2.000000      1.000000  200606.000000      1.000000   \n",
       "25%        1.000000      4.000000      2.000000  201211.000000      1.000000   \n",
       "50%        1.000000      4.000000      3.000000  201504.000000      1.000000   \n",
       "75%        2.000000      5.000000      3.000000  201702.000000      2.000000   \n",
       "max        8.000000      6.000000      3.000000  202105.000000      3.000000   \n",
       "\n",
       "               price  \n",
       "count   30000.000000  \n",
       "mean       18.062224  \n",
       "std       629.444049  \n",
       "min         0.050000  \n",
       "25%         6.100000  \n",
       "50%        10.479900  \n",
       "75%        18.000000  \n",
       "max    109000.000000  \n",
       "\n",
       "[8 rows x 29 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Train_data.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 30000 entries, 0 to 29999\n",
      "Data columns (total 36 columns):\n",
      " #   Column         Non-Null Count  Dtype  \n",
      "---  ------         --------------  -----  \n",
      " 0   cardid         30000 non-null  int64  \n",
      " 1   tradeTime      30000 non-null  object \n",
      " 2   brand          30000 non-null  int64  \n",
      " 3   serial         30000 non-null  int64  \n",
      " 4   model          30000 non-null  int64  \n",
      " 5   mileage        30000 non-null  float64\n",
      " 6   color          30000 non-null  int64  \n",
      " 7   cityId         30000 non-null  int64  \n",
      " 8   carCode        29991 non-null  float64\n",
      " 9   transferCount  30000 non-null  int64  \n",
      " 10  seatings       30000 non-null  int64  \n",
      " 11  registerDate   30000 non-null  object \n",
      " 12  licenseDate    30000 non-null  object \n",
      " 13  country        26243 non-null  float64\n",
      " 14  maketype       26359 non-null  float64\n",
      " 15  modelyear      29688 non-null  float64\n",
      " 16  displacement   30000 non-null  float64\n",
      " 17  gearbox        29999 non-null  float64\n",
      " 18  oiltype        30000 non-null  int64  \n",
      " 19  newprice       30000 non-null  float64\n",
      " 20  v1             28418 non-null  float64\n",
      " 21  v2             30000 non-null  int64  \n",
      " 22  v3             30000 non-null  int64  \n",
      " 23  v4             17892 non-null  float64\n",
      " 24  v5             30000 non-null  int64  \n",
      " 25  v6             30000 non-null  int64  \n",
      " 26  v7             11956 non-null  object \n",
      " 27  v8             26225 non-null  float64\n",
      " 28  v9             26256 non-null  float64\n",
      " 29  v10            23759 non-null  float64\n",
      " 30  v11            29539 non-null  object \n",
      " 31  v12            30000 non-null  object \n",
      " 32  v13            28381 non-null  float64\n",
      " 33  v14            30000 non-null  int64  \n",
      " 34  v15            2420 non-null   object \n",
      " 35  price          30000 non-null  float64\n",
      "dtypes: float64(15), int64(14), object(7)\n",
      "memory usage: 8.2+ MB\n"
     ]
    }
   ],
   "source": [
    "Train_data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "cardid               0\n",
       "tradeTime            0\n",
       "brand                0\n",
       "serial               0\n",
       "model                0\n",
       "mileage              0\n",
       "color                0\n",
       "cityId               0\n",
       "carCode              9\n",
       "transferCount        0\n",
       "seatings             0\n",
       "registerDate         0\n",
       "licenseDate          0\n",
       "country           3757\n",
       "maketype          3641\n",
       "modelyear          312\n",
       "displacement         0\n",
       "gearbox              1\n",
       "oiltype              0\n",
       "newprice             0\n",
       "v1                1582\n",
       "v2                   0\n",
       "v3                   0\n",
       "v4               12108\n",
       "v5                   0\n",
       "v6                   0\n",
       "v7               18044\n",
       "v8                3775\n",
       "v9                3744\n",
       "v10               6241\n",
       "v11                461\n",
       "v12                  0\n",
       "v13               1619\n",
       "v14                  0\n",
       "v15              27580\n",
       "price                0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Train_data.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "cardid              0\n",
       "tradeTime           0\n",
       "brand               0\n",
       "serial              0\n",
       "model               0\n",
       "mileage             0\n",
       "color               0\n",
       "cityId              0\n",
       "carCode             0\n",
       "transferCount       0\n",
       "seatings            0\n",
       "registerDate        0\n",
       "licenseDate         0\n",
       "country           396\n",
       "maketype          375\n",
       "modelyear         106\n",
       "displacement        0\n",
       "gearbox             0\n",
       "oiltype             0\n",
       "newprice            0\n",
       "v1                340\n",
       "v2                  0\n",
       "v3                  0\n",
       "v4               1863\n",
       "v5                  0\n",
       "v6                  0\n",
       "v7               3315\n",
       "v8                416\n",
       "v9                413\n",
       "v10              1231\n",
       "v11                73\n",
       "v12                 1\n",
       "v13               260\n",
       "v14                 0\n",
       "v15              4719\n",
       "price            5000\n",
       "dtype: int64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Test_data.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# nan可视化\n",
    "missing = Train_data.isnull().sum()\n",
    "missing = missing[missing > 0]\n",
    "missing.sort_values(inplace=True)\n",
    "missing.plot.bar()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1800x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "msno.matrix(Train_data.sample(250))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1728x720 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "msno.bar(Train_data.sample(1000))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1800x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "msno.matrix(Test_data.sample(250))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1728x720 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "msno.bar(Test_data.sample(1000))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# del Train_data['v15']\n",
    "# Train_data['v15'].isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0        NaN\n",
       "1        NaN\n",
       "2        NaN\n",
       "3        NaN\n",
       "4        NaN\n",
       "        ... \n",
       "29995    NaN\n",
       "29996    NaN\n",
       "29997    NaN\n",
       "29998    NaN\n",
       "29999    NaN\n",
       "Name: v15, Length: 30000, dtype: object"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# from numpy import NaN\n",
    "# Train_data['v15']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0         4.24\n",
       "1         7.38\n",
       "2         1.00\n",
       "3         4.38\n",
       "4         5.90\n",
       "         ...  \n",
       "29995     0.88\n",
       "29996     8.30\n",
       "29997     5.00\n",
       "29998    28.00\n",
       "29999    18.80\n",
       "Name: price, Length: 30000, dtype: float64"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Train_data['price']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count     30000.000000\n",
       "mean         18.062224\n",
       "std         629.444049\n",
       "min           0.050000\n",
       "25%           6.100000\n",
       "50%          10.479900\n",
       "75%          18.000000\n",
       "max      109000.000000\n",
       "Name: price, dtype: float64"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Train_data['price'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.sum((Train_data['price'] > 700))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "Train_data = Train_data[(Train_data['price'] < 700)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.sum((Train_data['price'] > 700))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:title={'center':'Log Normal'}, xlabel='price'>"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "## 1) 总体分布概况（无界约翰逊分布等）\n",
    "import scipy.stats as st\n",
    "y = Train_data['price']\n",
    "plt.figure(1); plt.title('Johnson SU')\n",
    "sns.distplot(y, kde=False, fit=st.johnsonsu)\n",
    "plt.figure(2); plt.title('Normal')\n",
    "sns.distplot(y, kde=False, fit=st.norm)\n",
    "plt.figure(3); plt.title('Log Normal')\n",
    "sns.distplot(y, kde=False, fit=st.lognorm)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Skewness: 11.365980\n",
      "Kurtosis: 312.158134\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "## 2) 查看skewness and kurtosis\n",
    "sns.distplot(Train_data['price']);\n",
    "print(\"Skewness: %f\" % Train_data['price'].skew())\n",
    "print(\"Kurtosis: %f\" % Train_data['price'].kurt())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(cardid           -0.003064\n",
       " brand             2.017830\n",
       " serial            1.609906\n",
       " model             0.257707\n",
       " mileage           1.040920\n",
       " color             0.912119\n",
       " cityId            2.507728\n",
       " carCode           1.520548\n",
       " transferCount     2.583062\n",
       " seatings          1.117863\n",
       " country         -19.569316\n",
       " maketype          0.017700\n",
       " modelyear        -0.550951\n",
       " displacement      2.217902\n",
       " gearbox           0.790347\n",
       " oiltype           7.785995\n",
       " newprice          5.700793\n",
       " v1               23.778172\n",
       " v2                1.182345\n",
       " v3               11.383434\n",
       " v4                1.450405\n",
       " v5                1.933252\n",
       " v6                2.836673\n",
       " v8                2.428426\n",
       " v9               -0.973816\n",
       " v10              -0.034854\n",
       " v13              -0.281884\n",
       " v14               0.500355\n",
       " price            11.365980\n",
       " dtype: float64,\n",
       " cardid            -1.200722\n",
       " brand              4.767573\n",
       " serial             2.378144\n",
       " model             -1.338142\n",
       " mileage            2.086896\n",
       " color             -0.235804\n",
       " cityId            11.227477\n",
       " carCode            2.184335\n",
       " transferCount     11.741466\n",
       " seatings           7.684652\n",
       " country          380.987176\n",
       " maketype           0.134736\n",
       " modelyear         -0.177729\n",
       " displacement       8.977032\n",
       " gearbox           -0.460280\n",
       " oiltype           84.099876\n",
       " newprice          68.308852\n",
       " v1               563.441107\n",
       " v2                 1.214516\n",
       " v3               149.956696\n",
       " v4                 1.962250\n",
       " v5                 4.276494\n",
       " v6                10.108535\n",
       " v8                 9.816971\n",
       " v9                 1.954250\n",
       " v10               -1.990893\n",
       " v13               -0.720889\n",
       " v14               -1.532530\n",
       " price            312.158134\n",
       " dtype: float64)"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Train_data.skew(), Train_data.kurt()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Skewness', ylabel='Density'>"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(Train_data.skew(),color='blue',axlabel ='Skewness')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Kurtness', ylabel='Density'>"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(Train_data.kurt(),color='orange',axlabel ='Kurtness')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "## 3) 查看预测值的具体频数\n",
    "plt.hist(Train_data['price'], orientation = 'vertical',histtype = 'bar', color ='red')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "99"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.sum(Train_data['price']>=100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYQAAAD4CAYAAADsKpHdAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuNCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8QVMy6AAAACXBIWXMAAAsTAAALEwEAmpwYAAAUIklEQVR4nO3dfZBdd33f8fenUlAMicDYC6PooRIgaG0mFdUdVS2FccekViiDTAYaMW2sSdwR9pgptOk0VvIHtDOdKU2IU88EZQR2JRPqhxiMNRmc4NiZuH/4gRW4lmzjsMYOWqRKiu0atWSckfj2j/tbcrW6Wq3urvfurd6vmTN77vc87Pfaq/u553fOuTdVhSRJf2vYDUiSFgcDQZIEGAiSpMZAkCQBBoIkqVk67AYGdemll9batWuH3YYkjZT9+/f/ZVWN9Vs2soGwdu1axsfHh92GJI2UJH9xtmUOGUmSAANBktQYCJIkwECQJDUGgiQJMBAkSY2BIEkCDARJUmMgSJKAEb5TWSMkGc7v9cufpPPiEYIkCTAQJEmNgSBJAmYRCEluTXIsycGe2p1JHm/T80keb/W1Sf6qZ9nv9WyzMcmBJBNJbk66A8tJlrX9TSR5NMna+X+akqRzmc0Rwh5gS2+hqn6xqjZU1Qbgy8BXehY/O7Wsqq7rqe8CdgDr2zS1z2uBl6rqbcBNwGcGeSKSpLk5ZyBU1UPAi/2WtXf5/xy4faZ9JFkBLK+qh6uqgNuAq9vircDeNn83cOXU0YMkaeHM9RzCe4CjVfWdntq6JN9K8mdJ3tNqK4HJnnUmW21q2SGAqjoJvAxcMse+JEnnaa73IXyU048OjgBrquqFJBuBrya5HOj3jn/qIvGZlp0myQ66w06sWbNm4KYlSWca+AghyVLgF4A7p2pV9UpVvdDm9wPPAm+ne0SwqmfzVcDhNj8JrO7Z5+s5yxBVVe2uqk5VdcbG+n4lqCRpQHMZMnof8O2q+vFQUJKxJEva/Fvonjz+blUdAU4k2dzOD1wD3Ns22wdsb/MfBh5s5xkkSQtoNped3g48DLwjyWSSa9uibZx5Mvm9wBNJ/ifdE8TXVdXUu/3rgS8AE3SPHO5r9VuAS5JMAP8WuHEOz0eSNKCM6pvxTqdT4+Pjw25Ds+FnGUmLRpL9VdXpt8w7lSVJgIEgSWoMBEkSYCBIkhoDQZIEGAiSpMZAkCQBBoIkqTEQJEmAgSBJagwESRJgIEiSGgNBkgQYCJKkxkCQJAEGgiSpMRAkSYCBIElqDARJEmAgSJKacwZCkluTHEtysKf26STfT/J4m97fs2xnkokkzyS5qqe+McmBtuzmpPvN60mWJbmz1R9Nsnaen6MkaRZmc4SwB9jSp35TVW1o09cAklwGbAMub9t8LsmStv4uYAewvk1T+7wWeKmq3gbcBHxmwOciSZqDcwZCVT0EvDjL/W0F7qiqV6rqOWAC2JRkBbC8qh6uqgJuA67u2WZvm78buHLq6EGStHDmcg7h40meaENKF7faSuBQzzqTrbayzU+vn7ZNVZ0EXgYu6fcLk+xIMp5k/Pjx43NoXZI03aCBsAt4K7ABOAJ8ttX7vbOvGeozbXNmsWp3VXWqqjM2NnZeDUuSZjZQIFTV0ao6VVU/Aj4PbGqLJoHVPauuAg63+qo+9dO2SbIUeD2zH6KSJM2TgQKhnROY8iFg6gqkfcC2duXQOronjx+rqiPAiSSb2/mBa4B7e7bZ3uY/DDzYzjNIkhbQ0nOtkOR24Arg0iSTwKeAK5JsoDu08zzwMYCqejLJXcBTwEnghqo61XZ1Pd0rli4C7msTwC3AF5NM0D0y2DYPz0uSdJ4yqm/GO51OjY+PD7sNzcawLhob0b9t6dWUZH9Vdfot805lSRJgIEiSGgNBkgQYCJKkxkCQJAEGgiSpMRAkSYCBIElqDARJEmAgSJIaA0GSBBgIkqTGQJAkAQaCJKkxECRJgIEgSWoMBEkSYCBIkhoDQZIEzCIQktya5FiSgz2130zy7SRPJLknyRtafW2Sv0ryeJt+r2ebjUkOJJlIcnPS/aLdJMuS3NnqjyZZO/9PU5J0LrM5QtgDbJlWux94Z1X9LPDnwM6eZc9W1YY2XddT3wXsANa3aWqf1wIvVdXbgJuAz5z3s5Akzdk5A6GqHgJenFb7elWdbA8fAVbNtI8kK4DlVfVwVRVwG3B1W7wV2Nvm7waunDp6kCQtnPk4h/ArwH09j9cl+VaSP0vynlZbCUz2rDPZalPLDgG0kHkZuGQe+pIknYelc9k4yW8AJ4EvtdIRYE1VvZBkI/DVJJcD/d7x19RuZlg2/fftoDvsxJo1a+bSuiRpmoGPEJJsBz4A/Is2DERVvVJVL7T5/cCzwNvpHhH0DiutAg63+UlgddvnUuD1TBuimlJVu6uqU1WdsbGxQVuXJPUxUCAk2QL8GvDBqvphT30syZI2/xa6J4+/W1VHgBNJNrfzA9cA97bN9gHb2/yHgQenAkaStHDOOWSU5HbgCuDSJJPAp+heVbQMuL+d/32kXVH0XuA/JjkJnAKuq6qpd/vX071i6SK65xymzjvcAnwxyQTdI4Nt8/LMJEnnJaP6ZrzT6dT4+Piw29BsDOuisRH925ZeTUn2V1Wn3zLvVJYkAQaCJKkxECRJgIEgSWoMBEkSYCBIkhoDQZIEGAiSpMZAkCQBBoIkqTEQJEmAgSBJagwESRJgIEiSGgNBkgQYCJKkxkCQJAEGgiSpMRAkSYCBIElqzhkISW5NcizJwZ7aG5Pcn+Q77efFPct2JplI8kySq3rqG5McaMtuTrrfvJ5kWZI7W/3RJGvn+TlKkmZhNkcIe4At02o3Ag9U1XrggfaYJJcB24DL2zafS7KkbbML2AGsb9PUPq8FXqqqtwE3AZ8Z9MlIkgZ3zkCoqoeAF6eVtwJ72/xe4Oqe+h1V9UpVPQdMAJuSrACWV9XDVVXAbdO2mdrX3cCVU0cPkqSFM+g5hDdX1RGA9vNNrb4SONSz3mSrrWzz0+unbVNVJ4GXgUv6/dIkO5KMJxk/fvz4gK1LkvqZ75PK/d7Z1wz1mbY5s1i1u6o6VdUZGxsbsEVJUj+DBsLRNgxE+3ms1SeB1T3rrQIOt/qqPvXTtkmyFHg9Zw5RSZJeZYMGwj5ge5vfDtzbU9/WrhxaR/fk8WNtWOlEks3t/MA107aZ2teHgQfbeQZJ0gJaeq4VktwOXAFcmmQS+BTwn4G7klwLfA/4CEBVPZnkLuAp4CRwQ1Wdaru6nu4VSxcB97UJ4Bbgi0km6B4ZbJuXZyZJOi8Z1TfjnU6nxsfHh92GZmNYF42N6N+29GpKsr+qOv2WeaeyJAkwECRJjYEgSQIMBElSYyBIkgADQZLUGAiSJMBAkCQ1BoIkCTAQJEmNgSBJAgwESVJjIEiSAANBktQYCJIkwECQJDUGgiQJmMVXaEojy29qk86LRwiSJGAOgZDkHUke75l+kOSTST6d5Ps99ff3bLMzyUSSZ5Jc1VPfmORAW3ZzMqy3dpJ04Ro4EKrqmaraUFUbgI3AD4F72uKbppZV1dcAklwGbAMuB7YAn0uypK2/C9gBrG/TlkH7kiQNZr6GjK4Enq2qv5hhna3AHVX1SlU9B0wAm5KsAJZX1cNVVcBtwNXz1JckaZbmKxC2Abf3PP54kieS3Jrk4lZbCRzqWWey1Va2+en1MyTZkWQ8yfjx48fnqXVJEsxDICR5DfBB4A9aaRfwVmADcAT47NSqfTavGepnFqt2V1WnqjpjY2NzaVuSNM18HCH8PPDNqjoKUFVHq+pUVf0I+Dywqa03Cazu2W4VcLjVV/WpS5IW0HwEwkfpGS5q5wSmfAg42Ob3AduSLEuyju7J48eq6ghwIsnmdnXRNcC989CXJOk8zOnGtCSvBX4O+FhP+b8k2UB32Of5qWVV9WSSu4CngJPADVV1qm1zPbAHuAi4r02SpAWUGtG7KjudTo2Pjw+7Dc3GhXZbyYj+m9KFIcn+qur0W+adypIkwECQJDUGgiQJMBAkSY2BIEkCDARJUmMgSJIAA0GS1BgIkiTAQJAkNQaCJAkwECRJjYEgSQIMBElSYyBIkgADQZLUGAiSJMBAkCQ1BoIkCTAQJEnNnAIhyfNJDiR5PMl4q70xyf1JvtN+Xtyz/s4kE0meSXJVT31j289EkpuTC+1b2SVp+ObjCOGfVNWGquq0xzcCD1TVeuCB9pgklwHbgMuBLcDnkixp2+wCdgDr27RlHvqSJJ2HV2PIaCuwt83vBa7uqd9RVa9U1XPABLApyQpgeVU9XFUF3NazjSRpgcw1EAr4epL9SXa02pur6ghA+/mmVl8JHOrZdrLVVrb56fUzJNmRZDzJ+PHjx+fYuiSp19I5bv/uqjqc5E3A/Um+PcO6/c4L1Az1M4tVu4HdAJ1Op+86kqTBzOkIoaoOt5/HgHuATcDRNgxE+3msrT4JrO7ZfBVwuNVX9alLkhbQwIGQ5HVJfnpqHvinwEFgH7C9rbYduLfN7wO2JVmWZB3dk8ePtWGlE0k2t6uLrunZRpK0QOYyZPRm4J52hehS4L9X1R8l+QZwV5Jrge8BHwGoqieT3AU8BZwEbqiqU21f1wN7gIuA+9okSVpA6V7YM3o6nU6Nj48Puw3NxoV2W8mI/pvShSHJ/p7bBE7jncqSJMBAkCQ1BoIkCTAQJEmNgSBJAgwESVJjIEiSAANBktQYCJIkwECQJDUGgiQJMBAkSc1cvyBHo+JC+4A5SefNIwRJEmAgSJIaA0GSBBgIkqTGQJAkAQaCJKkZOBCSrE7yp0meTvJkkk+0+qeTfD/J4216f882O5NMJHkmyVU99Y1JDrRlNydeIylJC20u9yGcBH61qr6Z5KeB/Unub8tuqqrf6l05yWXANuBy4GeAP0ny9qo6BewCdgCPAF8DtgD3zaE3SdJ5GvgIoaqOVNU32/wJ4Glg5QybbAXuqKpXquo5YALYlGQFsLyqHq6qAm4Drh60L0nSYOblHEKStcC7gEdb6eNJnkhya5KLW20lcKhns8lWW9nmp9f7/Z4dScaTjB8/fnw+WpckNXMOhCQ/BXwZ+GRV/YDu8M9bgQ3AEeCzU6v22bxmqJ9ZrNpdVZ2q6oyNjc21dUlSjzkFQpKfoBsGX6qqrwBU1dGqOlVVPwI+D2xqq08Cq3s2XwUcbvVVfeqSpAU0l6uMAtwCPF1Vv91TX9Gz2oeAg21+H7AtybIk64D1wGNVdQQ4kWRz2+c1wL2D9iVJGsxcrjJ6N/BLwIEkj7farwMfTbKB7rDP88DHAKrqySR3AU/RvULphnaFEcD1wB7gIrpXF3mFkSQtsHQv7Bk9nU6nxsfHh93G6PDWjoUzov+mdGFIsr+qOv2WeaeyJAkwECRJjYEgSQIMBElSYyBIkgADQZLUGAiSJMBAkCQ1BoIkCTAQJEmNgSBJAgwESVJjIEiSAANBktQYCJIkwECQJDUGgiQJMBAkSY2BIEkCDARJUrNoAiHJliTPJJlIcuOw+5EGlgxvkuZg6bAbAEiyBPhd4OeASeAbSfZV1VPD7exV4D9aSYvUYjlC2ARMVNV3q+qvgTuArUPuSZIuKIviCAFYCRzqeTwJ/IPpKyXZAexoD/9PkmcWoLfZuhT4y2E3MQuj0Oco9AiLsc/+R6CLr8/+RqHPUegRZu7zb59to8USCP3+iuuMQtVuYPer3875SzJeVZ1h93Euo9DnKPQI9jnfRqHPUegRBu9zsQwZTQKrex6vAg4PqRdJuiAtlkD4BrA+ybokrwG2AfuG3JMkXVAWxZBRVZ1M8nHgj4ElwK1V9eSQ2zpfi3Ioq49R6HMUegT7nG+j0Oco9AgD9pmqM4bqJUkXoMUyZCRJGjIDQZIEGAjzKslvJvl2kieS3JPkDcPuacoofDRIktVJ/jTJ00meTPKJYfd0NkmWJPlWkj8cdi9nk+QNSe5uf5NPJ/mHw+6pnyT/pv3/Ppjk9iQ/OeyeAJLcmuRYkoM9tTcmuT/Jd9rPi4fZY+upX58DvRYZCPPrfuCdVfWzwJ8DO4fcD3DaR4P8PHAZ8NEklw23q75OAr9aVX8X2AzcsEj7BPgE8PSwmziH/wr8UVX9HeDvsQj7TbIS+NdAp6reSfeikm3D7erH9gBbptVuBB6oqvXAA+3xsO3hzD4Hei0yEOZRVX29qk62h4/QvZ9iMRiJjwapqiNV9c02f4LuC9jK4XZ1piSrgH8GfGHYvZxNkuXAe4FbAKrqr6vqfw+1qbNbClyUZCnwWhbJPUhV9RDw4rTyVmBvm98LXL2QPfXTr89BX4sMhFfPrwD3DbuJpt9Hgyy6F9peSdYC7wIeHXIr/fwO8O+BHw25j5m8BTgO/Lc2tPWFJK8bdlPTVdX3gd8CvgccAV6uqq8Pt6sZvbmqjkD3DQzwpiH3Mxuzfi0yEM5Tkj9pY53Tp6096/wG3eGPLw2v09PM6qNBFoskPwV8GfhkVf1g2P30SvIB4FhV7R92L+ewFPj7wK6qehfwf1kcwxunaWPwW4F1wM8Ar0vyL4fb1f8/zve1aFHcmDZKqup9My1Psh34AHBlLZ6bPEbmo0GS/ATdMPhSVX1l2P308W7gg0neD/wksDzJ71fVYnsRmwQmq2rqCOtuFmEgAO8Dnquq4wBJvgL8I+D3h9rV2R1NsqKqjiRZARwbdkNnM8hrkUcI8yjJFuDXgA9W1Q+H3U+PkfhokCShO+b9dFX99rD76aeqdlbVqqpaS/e/44OLMAyoqv8FHEryjla6EliM3y/yPWBzkte2//9XsghPfvfYB2xv89uBe4fYy1kN+lrkncrzKMkEsAx4oZUeqarrhtjSj7V3tL/D33w0yH8abkdnSvKPgf8BHOBvxud/vaq+Nryuzi7JFcC/q6oPDLmVvpJsoHvi+zXAd4FfrqqXhtpUH0n+A/CLdIc2vgX8q6p6ZbhdQZLbgSvofpT0UeBTwFeBu4A1dMPsI1U1/cTzgjpLnzsZ4LXIQJAkAQ4ZSZIaA0GSBBgIkqTGQJAkAQaCJKkxECRJgIEgSWr+HzHK+8G3rQUaAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(np.log(Train_data['price']), orientation = 'vertical',histtype = 'bar', color ='red') \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 分离label即预测值\n",
    "Y_train = Train_data['price']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [],
   "source": [
    "numeric_features = ['transferCount', 'mileage', 'seatings','modelyear','newprice', 'v1', 'v2', 'v3', 'v4', 'v5', 'v6', 'v8', 'v9', 'v10', 'v12_0',\"v12_1\",\"v12_2\", 'v13','v14','tradeTime_y','licenseDate_y' ]\n",
    "categorical_features = [ 'model', 'brand', 'serial', 'color', 'cityId', 'carCode', 'registerDate_d','registerDate_m','country','v7','v11','maketype','gearbox','oiltype','tradeTime_d','tradeTime_m','licenseDate_d','licenseDate_m']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "cardid的特征分布如下：\n",
      "cardid特征有个30000不同的值\n",
      "65536    1\n",
      "5015     1\n",
      "68532    1\n",
      "13235    1\n",
      "9137     1\n",
      "        ..\n",
      "32101    1\n",
      "30052    1\n",
      "19811    1\n",
      "17762    1\n",
      "69630    1\n",
      "Name: cardid, Length: 30000, dtype: int64\n",
      "model的特征分布如下：\n",
      "model特征有个8754不同的值\n",
      "6717     85\n",
      "86       82\n",
      "34       77\n",
      "306      75\n",
      "338      72\n",
      "         ..\n",
      "7543      1\n",
      "11633     1\n",
      "2987      1\n",
      "9150      1\n",
      "11558     1\n",
      "Name: model, Length: 8754, dtype: int64\n",
      "brand的特征分布如下：\n",
      "brand特征有个125不同的值\n",
      "8      3836\n",
      "20     2439\n",
      "18     2432\n",
      "11     2170\n",
      "12     1783\n",
      "       ... \n",
      "120       1\n",
      "104       1\n",
      "21        1\n",
      "70        1\n",
      "95        1\n",
      "Name: brand, Length: 125, dtype: int64\n",
      "serial的特征分布如下：\n",
      "serial特征有个1080不同的值\n",
      "175     612\n",
      "127     593\n",
      "33      572\n",
      "15      569\n",
      "63      502\n",
      "       ... \n",
      "1003      1\n",
      "987       1\n",
      "1171      1\n",
      "1187      1\n",
      "1231      1\n",
      "Name: serial, Length: 1080, dtype: int64\n",
      "color的特征分布如下：\n",
      "color特征有个10不同的值\n",
      "1     12179\n",
      "2      6011\n",
      "5      3918\n",
      "6      2616\n",
      "4      2221\n",
      "3      1779\n",
      "7       633\n",
      "8       350\n",
      "9       268\n",
      "10       25\n",
      "Name: color, dtype: int64\n",
      "cityId的特征分布如下：\n",
      "cityId特征有个88不同的值\n",
      "1     12601\n",
      "7      2836\n",
      "17     2210\n",
      "3      1792\n",
      "4      1467\n",
      "      ...  \n",
      "83        1\n",
      "92        1\n",
      "87        1\n",
      "85        1\n",
      "91        1\n",
      "Name: cityId, Length: 88, dtype: int64\n",
      "carCode的特征分布如下：\n",
      "carCode特征有个6不同的值\n",
      "1.0    16069\n",
      "2.0    11269\n",
      "4.0     1711\n",
      "3.0      906\n",
      "5.0       34\n",
      "6.0        2\n",
      "Name: carCode, dtype: int64\n",
      "registerDate_d的特征分布如下：\n"
     ]
    },
    {
     "ename": "KeyError",
     "evalue": "'registerDate_d'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[1;32m~\\anaconda3\\envs\\dl\\lib\\site-packages\\pandas\\core\\indexes\\base.py\u001b[0m in \u001b[0;36mget_loc\u001b[1;34m(self, key, method, tolerance)\u001b[0m\n\u001b[0;32m   3079\u001b[0m             \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 3080\u001b[1;33m                 \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcasted_key\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   3081\u001b[0m             \u001b[1;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0merr\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mpandas\\_libs\\index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;32mpandas\\_libs\\index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;32mpandas\\_libs\\hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;32mpandas\\_libs\\hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;31mKeyError\u001b[0m: 'registerDate_d'",
      "\nThe above exception was the direct cause of the following exception:\n",
      "\u001b[1;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-67-b886a3016cbf>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      2\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mcat_fea\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mcategorical_features\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      3\u001b[0m     \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcat_fea\u001b[0m \u001b[1;33m+\u001b[0m \u001b[1;34m\"的特征分布如下：\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 4\u001b[1;33m     \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"{}特征有个{}不同的值\"\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcat_fea\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mTrain_data\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mcat_fea\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mnunique\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      5\u001b[0m     \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mTrain_data\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mcat_fea\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mvalue_counts\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\envs\\dl\\lib\\site-packages\\pandas\\core\\frame.py\u001b[0m in \u001b[0;36m__getitem__\u001b[1;34m(self, key)\u001b[0m\n\u001b[0;32m   3022\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mnlevels\u001b[0m \u001b[1;33m>\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   3023\u001b[0m                 \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_getitem_multilevel\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 3024\u001b[1;33m             \u001b[0mindexer\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   3025\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mis_integer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mindexer\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   3026\u001b[0m                 \u001b[0mindexer\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[0mindexer\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\envs\\dl\\lib\\site-packages\\pandas\\core\\indexes\\base.py\u001b[0m in \u001b[0;36mget_loc\u001b[1;34m(self, key, method, tolerance)\u001b[0m\n\u001b[0;32m   3080\u001b[0m                 \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcasted_key\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   3081\u001b[0m             \u001b[1;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0merr\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 3082\u001b[1;33m                 \u001b[1;32mraise\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mfrom\u001b[0m \u001b[0merr\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   3083\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   3084\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mtolerance\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mKeyError\u001b[0m: 'registerDate_d'"
     ]
    }
   ],
   "source": [
    "# 特征nunique分布\n",
    "for cat_fea in categorical_features:\n",
    "    print(cat_fea + \"的特征分布如下：\")\n",
    "    print(\"{}特征有个{}不同的值\".format(cat_fea, Train_data[cat_fea].nunique()))\n",
    "    print(Train_data[cat_fea].value_counts())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "numeric_features.append('price')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>cardid</th>\n",
       "      <th>tradeTime</th>\n",
       "      <th>brand</th>\n",
       "      <th>serial</th>\n",
       "      <th>model</th>\n",
       "      <th>mileage</th>\n",
       "      <th>color</th>\n",
       "      <th>cityId</th>\n",
       "      <th>carCode</th>\n",
       "      <th>transferCount</th>\n",
       "      <th>...</th>\n",
       "      <th>v12_2</th>\n",
       "      <th>tradeTime_y</th>\n",
       "      <th>tradeTime_m</th>\n",
       "      <th>tradeTime_d</th>\n",
       "      <th>licenseDate_y</th>\n",
       "      <th>licenseDate_m</th>\n",
       "      <th>licenseDate_d</th>\n",
       "      <th>registerDate_y</th>\n",
       "      <th>registerDate_m</th>\n",
       "      <th>registerDate_d</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>2021-06-28</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4.01</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1625</td>\n",
       "      <td>2021</td>\n",
       "      <td>6</td>\n",
       "      <td>28</td>\n",
       "      <td>2018</td>\n",
       "      <td>1</td>\n",
       "      <td>26</td>\n",
       "      <td>2017</td>\n",
       "      <td>12</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>2021-06-25</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>8.60</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1480</td>\n",
       "      <td>2021</td>\n",
       "      <td>6</td>\n",
       "      <td>25</td>\n",
       "      <td>2017</td>\n",
       "      <td>3</td>\n",
       "      <td>21</td>\n",
       "      <td>2016</td>\n",
       "      <td>12</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>5</td>\n",
       "      <td>2021-06-19</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>15.56</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1445</td>\n",
       "      <td>2021</td>\n",
       "      <td>6</td>\n",
       "      <td>19</td>\n",
       "      <td>2008</td>\n",
       "      <td>2</td>\n",
       "      <td>27</td>\n",
       "      <td>2008</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>6</td>\n",
       "      <td>2021-06-29</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>6.04</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3</td>\n",
       "      <td>...</td>\n",
       "      <td>1707</td>\n",
       "      <td>2021</td>\n",
       "      <td>6</td>\n",
       "      <td>29</td>\n",
       "      <td>2016</td>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "      <td>2016</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>7</td>\n",
       "      <td>2021-06-30</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>5.70</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>1606</td>\n",
       "      <td>2021</td>\n",
       "      <td>6</td>\n",
       "      <td>30</td>\n",
       "      <td>2012</td>\n",
       "      <td>8</td>\n",
       "      <td>28</td>\n",
       "      <td>2012</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 47 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   cardid   tradeTime  brand  serial  model  mileage  color  cityId  carCode  \\\n",
       "0       1  2021-06-28      1       1      1     4.01      1       1      1.0   \n",
       "1       2  2021-06-25      2       2      2     8.60      1       2      1.0   \n",
       "2       5  2021-06-19      5       5      5    15.56      1       2      3.0   \n",
       "3       6  2021-06-29      6       6      6     6.04      1       3      1.0   \n",
       "4       7  2021-06-30      7       7      7     5.70      4       1      2.0   \n",
       "\n",
       "   transferCount  ...  v12_2 tradeTime_y tradeTime_m  tradeTime_d  \\\n",
       "0              0  ...   1625        2021           6           28   \n",
       "1              0  ...   1480        2021           6           25   \n",
       "2              0  ...   1445        2021           6           19   \n",
       "3              3  ...   1707        2021           6           29   \n",
       "4              2  ...   1606        2021           6           30   \n",
       "\n",
       "   licenseDate_y  licenseDate_m  licenseDate_d  registerDate_y  \\\n",
       "0           2018              1             26            2017   \n",
       "1           2017              3             21            2016   \n",
       "2           2008              2             27            2008   \n",
       "3           2016              9              9            2016   \n",
       "4           2012              8             28            2012   \n",
       "\n",
       "   registerDate_m  registerDate_d  \n",
       "0              12               1  \n",
       "1              12               1  \n",
       "2               2               1  \n",
       "3               8               1  \n",
       "4               8               1  \n",
       "\n",
       "[5 rows x 47 columns]"
      ]
     },
     "execution_count": 156,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# numeric_features\n",
    "Train_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "def split(data):\n",
    "    data['v12_0'] = data['v12'].apply(lambda x: x.split(\"*\")[0])\n",
    "    data['v12_1'] = data['v12'].apply(lambda x: x.split(\"*\")[1])\n",
    "    data['v12_2'] = data['v12'].apply(lambda x: x.split(\"*\")[2])\n",
    "\n",
    "    # data['tradeTime_y'] = data['tradeTime'].apply(lambda x: int(x.split(\"-\")[0]))\n",
    "    # data['tradeTime_m'] = data['tradeTime'].apply(lambda x: int(x.split(\"-\")[1]))\n",
    "    # data['tradeTime_d'] = data['tradeTime'].apply(lambda x: int(x.split(\"-\")[2]))\n",
    "\n",
    "    # data['licenseDate_y'] = data['licenseDate'].apply(lambda x: int(x.split(\"-\")[0]))\n",
    "    # data['licenseDate_m'] = data['licenseDate'].apply(lambda x: int(x.split(\"-\")[1]))\n",
    "    # data['licenseDate_d'] = data['licenseDate'].apply(lambda x: int(x.split(\"-\")[2]))\n",
    "\n",
    "    # data['registerDate_y'] = data['registerDate'].apply(lambda x: int(x.split(\"-\")[0]))\n",
    "    # data['registerDate_m'] = data['registerDate'].apply(lambda x: int(x.split(\"-\")[1]))\n",
    "    # data['registerDate_d'] = data['registerDate'].apply(lambda x: int(x.split(\"-\")[2]))\n",
    "    \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Test_data[Test_data[\"v12\"].apply(lambda x: type(x)==float)]\n",
    "Test_data.loc[4102,'v12']=\"0*0*0\"\n",
    "split(Train_data)\n",
    "split(Test_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Test_data[Test_data[\"v12\"].apply(lambda x: type(x)==float)]['v12']=\"0*0*0\"\n",
    "# del Train_data['v15']\n",
    "# del Train_data['v7']\n",
    "# del Test_data['v7']\n",
    "# del Test_data['v15']\n",
    "# Train_data.fillna(method=\"bfill\",inplace=True)\n",
    "# Test_data.fillna(method=\"bfill\",inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "split(Train_data)\n",
    "split(Test_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "price            1.000000\n",
      "newprice         0.764698\n",
      "v8               0.463068\n",
      "v2               0.447126\n",
      "licenseDate_y    0.272029\n",
      "modelyear        0.263382\n",
      "v13              0.243729\n",
      "v5               0.239377\n",
      "v4               0.154579\n",
      "v6               0.058575\n",
      "transferCount    0.051490\n",
      "seatings         0.012305\n",
      "v1              -0.002376\n",
      "v3              -0.009130\n",
      "tradeTime_y     -0.010610\n",
      "v14             -0.021310\n",
      "v9              -0.023157\n",
      "v10             -0.049377\n",
      "mileage         -0.171435\n",
      "Name: price, dtype: float64 \n",
      "\n"
     ]
    }
   ],
   "source": [
    "## 1) 相关性分析\n",
    "price_numeric = Train_data[numeric_features]\n",
    "correlation = price_numeric.corr()\n",
    "print(correlation['price'].sort_values(ascending = False),'\\n')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:title={'center':'Correlation of Numeric Features with Price'}>"
      ]
     },
     "execution_count": 160,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 504x504 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "f , ax = plt.subplots(figsize = (7, 7))\n",
    "\n",
    "plt.title('Correlation of Numeric Features with Price',y=1,size=16)\n",
    "\n",
    "sns.heatmap(correlation,square = True,  vmax=0.8)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "metadata": {},
   "outputs": [],
   "source": [
    "del price_numeric['price']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "transferCount   Skewness: 02.58     Kurtosis: 011.74\n",
      "mileage         Skewness: 01.04     Kurtosis: 002.09\n",
      "seatings        Skewness: 01.12     Kurtosis: 007.68\n",
      "modelyear       Skewness: -0.55     Kurtosis: -00.18\n",
      "newprice        Skewness: 05.70     Kurtosis: 068.31\n",
      "v1              Skewness: 23.78     Kurtosis: 563.44\n",
      "v2              Skewness: 01.18     Kurtosis: 001.21\n",
      "v3              Skewness: 11.38     Kurtosis: 149.96\n",
      "v4              Skewness: 01.45     Kurtosis: 001.96\n",
      "v5              Skewness: 01.93     Kurtosis: 004.28\n",
      "v6              Skewness: 02.84     Kurtosis: 010.11\n",
      "v8              Skewness: 02.43     Kurtosis: 009.82\n",
      "v9              Skewness: -0.97     Kurtosis: 001.95\n",
      "v10             Skewness: -0.03     Kurtosis: -01.99\n",
      "v12_0           Skewness: -1.08     Kurtosis: 004.73\n",
      "v12_1           Skewness: -0.08     Kurtosis: 001.78\n",
      "v12_2           Skewness: 01.01     Kurtosis: 000.55\n",
      "v13             Skewness: -0.28     Kurtosis: -00.72\n",
      "v14             Skewness: 00.50     Kurtosis: -01.53\n",
      "tradeTime_y     Skewness: 00.18     Kurtosis: -01.97\n",
      "licenseDate_y   Skewness: -0.57     Kurtosis: -00.27\n",
      "price           Skewness: 11.37     Kurtosis: 312.16\n"
     ]
    }
   ],
   "source": [
    "## 2) 查看几个特征得 偏度和峰值\n",
    "for col in numeric_features:\n",
    "    print('{:15}'.format(col), \n",
    "          'Skewness: {:05.2f}'.format(Train_data[col].skew()) , \n",
    "          '   ' ,\n",
    "          'Kurtosis: {:06.2f}'.format(Train_data[col].kurt())  \n",
    "         )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x2376 with 22 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "## 3) 每个数字特征得分布可视化\n",
    "f = pd.melt(Train_data, value_vars=numeric_features)\n",
    "g = sns.FacetGrid(f, col=\"variable\",  col_wrap=2, sharex=False, sharey=False)\n",
    "g = g.map(sns.distplot, \"value\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['cardid', 'tradeTime', 'brand', 'serial', 'model', 'mileage', 'color',\n",
       "       'cityId', 'carCode', 'transferCount', 'seatings', 'registerDate',\n",
       "       'licenseDate', 'country', 'maketype', 'modelyear', 'displacement',\n",
       "       'gearbox', 'oiltype', 'newprice', 'v1', 'v2', 'v3', 'v4', 'v5', 'v6',\n",
       "       'v7', 'v8', 'v9', 'v10', 'v11', 'v12', 'v13', 'v14', 'price'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Train_data.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "model\n",
      "8754\n",
      "brand\n",
      "125\n",
      "serial\n",
      "1080\n",
      "color\n",
      "10\n",
      "cityId\n",
      "88\n",
      "carCode\n",
      "6\n",
      "registerDate_d\n",
      "1\n",
      "registerDate_m\n",
      "12\n",
      "country\n",
      "11\n",
      "v7\n",
      "1954\n",
      "v11\n",
      "6\n",
      "maketype\n",
      "3\n",
      "gearbox\n",
      "40\n",
      "oiltype\n",
      "5\n",
      "tradeTime_d\n",
      "31\n",
      "tradeTime_m\n",
      "12\n",
      "licenseDate_d\n",
      "31\n",
      "licenseDate_m\n",
      "12\n"
     ]
    }
   ],
   "source": [
    "## 1) unique分布\n",
    "for fea in categorical_features:\n",
    "    print(fea)\n",
    "    print(Train_data[fea].nunique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [],
   "source": [
    "categorical_features=['cardid',\n",
    " 'brand',\n",
    " 'color',\n",
    " 'cityId',\n",
    " 'carCode',\n",
    " 'registerDate_d',\n",
    " 'registerDate_m',\n",
    " 'country',\n",
    " 'v7',\n",
    " 'v11',\n",
    " 'maketype',\n",
    " 'gearbox',\n",
    " 'oiltype',\n",
    " 'tradeTime_d',\n",
    " 'tradeTime_m',\n",
    " 'licenseDate_d',\n",
    " 'licenseDate_m']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "## 2) 类别特征箱形图可视化\n",
    "\n",
    "# 因为 name和 regionCode的类别太稀疏了，这里我们把不稀疏的几类画一下\n",
    "\n",
    "for c in categorical_features:\n",
    "    Train_data[c] = Train_data[c].astype('category')\n",
    "    if Train_data[c].isnull().any():\n",
    "        Train_data[c] = Train_data[c].cat.add_categories(['MISSING'])\n",
    "        Train_data[c] = Train_data[c].fillna('MISSING')\n",
    "\n",
    "def boxplot(x, y, **kwargs):\n",
    "    sns.boxplot(x=x, y=y)\n",
    "    x=plt.xticks(rotation=90)\n",
    "\n",
    "f = pd.melt(Train_data, id_vars=['price'], value_vars=categorical_features)\n",
    "g = sns.FacetGrid(f, col=\"variable\",  col_wrap=2, sharex=False, sharey=False, size=5)\n",
    "g = g.map(boxplot, \"value\", \"price\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "## 3) 类别特征的小提琴图可视化\n",
    "catg_list = categorical_features\n",
    "target = 'price'\n",
    "for catg in catg_list :\n",
    "    sns.violinplot(x=catg, y=target, data=Train_data)\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "## 4) 类别特征的柱形图可视化\n",
    "def bar_plot(x, y, **kwargs):\n",
    "    sns.barplot(x=x, y=y)\n",
    "    x=plt.xticks(rotation=90)\n",
    "\n",
    "f = pd.melt(Train_data, id_vars=['price'], value_vars=categorical_features)\n",
    "g = sns.FacetGrid(f, col=\"variable\",  col_wrap=2, sharex=False, sharey=False, size=5)\n",
    "g = g.map(bar_plot, \"value\", \"price\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "##  5) 类别特征的每个类别频数可视化(count_plot)\n",
    "def count_plot(x,  **kwargs):\n",
    "    sns.countplot(x=x)\n",
    "    x=plt.xticks(rotation=90)\n",
    "\n",
    "f = pd.melt(Train_data,  value_vars=categorical_features)\n",
    "g = sns.FacetGrid(f, col=\"variable\",  col_wrap=2, sharex=False, sharey=False, size=5)\n",
    "g = g.map(count_plot, \"value\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas_profiling\n",
    "pfr = pandas_profiling.ProfileReport(Train_data)\n",
    "pfr.to_file(\"./example.html\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 这里我包装了一个异常值处理的代码，可以随便调用。\n",
    "def outliers_proc(data, col_name, scale=3):\n",
    "    \"\"\"\n",
    "    用于清洗异常值，默认用 box_plot（scale=3）进行清洗\n",
    "    :param data: 接收 pandas 数据格式\n",
    "    :param col_name: pandas 列名\n",
    "    :param scale: 尺度\n",
    "    :return:\n",
    "    \"\"\"\n",
    "\n",
    "    def box_plot_outliers(data_ser, box_scale):\n",
    "        \"\"\"\n",
    "        利用箱线图去除异常值\n",
    "        :param data_ser: 接收 pandas.Series 数据格式\n",
    "        :param box_scale: 箱线图尺度，\n",
    "        :return:\n",
    "        \"\"\"\n",
    "        iqr = box_scale * (data_ser.quantile(0.75) - data_ser.quantile(0.25))\n",
    "        val_low = data_ser.quantile(0.25) - iqr\n",
    "        val_up = data_ser.quantile(0.75) + iqr\n",
    "        rule_low = (data_ser < val_low)\n",
    "        rule_up = (data_ser > val_up)\n",
    "        return (rule_low, rule_up), (val_low, val_up)\n",
    "\n",
    "    data_n = data.copy()\n",
    "    data_series = data_n[col_name]\n",
    "    rule, value = box_plot_outliers(data_series, box_scale=scale)\n",
    "    index = np.arange(data_series.shape[0])[rule[0] | rule[1]]\n",
    "    print(\"Delete number is: {}\".format(len(index)))\n",
    "    data_n = data_n.drop(index)\n",
    "    data_n.reset_index(drop=True, inplace=True)\n",
    "    print(\"Now column number is: {}\".format(data_n.shape[0]))\n",
    "    index_low = np.arange(data_series.shape[0])[rule[0]]\n",
    "    outliers = data_series.iloc[index_low]\n",
    "    print(\"Description of data less than the lower bound is:\")\n",
    "    print(pd.Series(outliers).describe())\n",
    "    index_up = np.arange(data_series.shape[0])[rule[1]]\n",
    "    outliers = data_series.iloc[index_up]\n",
    "    print(\"Description of data larger than the upper bound is:\")\n",
    "    print(pd.Series(outliers).describe())\n",
    "    \n",
    "    fig, ax = plt.subplots(1, 2, figsize=(10, 7))\n",
    "    sns.boxplot(y=data[col_name], data=data, palette=\"Set1\", ax=ax[0])\n",
    "    sns.boxplot(y=data_n[col_name], data=data_n, palette=\"Set1\", ax=ax[1])\n",
    "    return data_n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>cardid</th>\n",
       "      <th>tradeTime</th>\n",
       "      <th>brand</th>\n",
       "      <th>serial</th>\n",
       "      <th>model</th>\n",
       "      <th>mileage</th>\n",
       "      <th>color</th>\n",
       "      <th>cityId</th>\n",
       "      <th>carCode</th>\n",
       "      <th>transferCount</th>\n",
       "      <th>...</th>\n",
       "      <th>price</th>\n",
       "      <th>tradeTime_y</th>\n",
       "      <th>tradeTime_m</th>\n",
       "      <th>tradeTime_d</th>\n",
       "      <th>licenseDate_y</th>\n",
       "      <th>licenseDate_m</th>\n",
       "      <th>licenseDate_d</th>\n",
       "      <th>registerDate_y</th>\n",
       "      <th>registerDate_m</th>\n",
       "      <th>registerDate_d</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>2021-06-28</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4.01</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>4.24</td>\n",
       "      <td>2021</td>\n",
       "      <td>6</td>\n",
       "      <td>28</td>\n",
       "      <td>2018</td>\n",
       "      <td>1</td>\n",
       "      <td>26</td>\n",
       "      <td>2017</td>\n",
       "      <td>12</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>2021-06-25</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>8.60</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>7.38</td>\n",
       "      <td>2021</td>\n",
       "      <td>6</td>\n",
       "      <td>25</td>\n",
       "      <td>2017</td>\n",
       "      <td>3</td>\n",
       "      <td>21</td>\n",
       "      <td>2016</td>\n",
       "      <td>12</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>5</td>\n",
       "      <td>2021-06-19</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>15.56</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.00</td>\n",
       "      <td>2021</td>\n",
       "      <td>6</td>\n",
       "      <td>19</td>\n",
       "      <td>2008</td>\n",
       "      <td>2</td>\n",
       "      <td>27</td>\n",
       "      <td>2008</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>6</td>\n",
       "      <td>2021-06-29</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>6.04</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3</td>\n",
       "      <td>...</td>\n",
       "      <td>4.38</td>\n",
       "      <td>2021</td>\n",
       "      <td>6</td>\n",
       "      <td>29</td>\n",
       "      <td>2016</td>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "      <td>2016</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>7</td>\n",
       "      <td>2021-06-30</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>5.70</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>5.90</td>\n",
       "      <td>2021</td>\n",
       "      <td>6</td>\n",
       "      <td>30</td>\n",
       "      <td>2012</td>\n",
       "      <td>8</td>\n",
       "      <td>28</td>\n",
       "      <td>2012</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 44 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "  cardid   tradeTime brand  serial  model  mileage color cityId carCode  \\\n",
       "0      1  2021-06-28     1       1      1     4.01     1      1     1.0   \n",
       "1      2  2021-06-25     2       2      2     8.60     1      2     1.0   \n",
       "2      5  2021-06-19     5       5      5    15.56     1      2     3.0   \n",
       "3      6  2021-06-29     6       6      6     6.04     1      3     1.0   \n",
       "4      7  2021-06-30     7       7      7     5.70     4      1     2.0   \n",
       "\n",
       "   transferCount  ...  price tradeTime_y tradeTime_m tradeTime_d  \\\n",
       "0              0  ...   4.24        2021           6          28   \n",
       "1              0  ...   7.38        2021           6          25   \n",
       "2              0  ...   1.00        2021           6          19   \n",
       "3              3  ...   4.38        2021           6          29   \n",
       "4              2  ...   5.90        2021           6          30   \n",
       "\n",
       "  licenseDate_y  licenseDate_m  licenseDate_d registerDate_y registerDate_m  \\\n",
       "0          2018              1             26           2017             12   \n",
       "1          2017              3             21           2016             12   \n",
       "2          2008              2             27           2008              2   \n",
       "3          2016              9              9           2016              8   \n",
       "4          2012              8             28           2012              8   \n",
       "\n",
       "   registerDate_d  \n",
       "0               1  \n",
       "1               1  \n",
       "2               1  \n",
       "3               1  \n",
       "4               1  \n",
       "\n",
       "[5 rows x 44 columns]"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Train_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Delete number is: 126\n",
      "Now column number is: 29874\n",
      "Description of data less than the lower bound is:\n",
      "count    0.0\n",
      "mean     NaN\n",
      "std      NaN\n",
      "min      NaN\n",
      "25%      NaN\n",
      "50%      NaN\n",
      "75%      NaN\n",
      "max      NaN\n",
      "Name: transferCount, dtype: float64\n",
      "Description of data larger than the upper bound is:\n",
      "count    126.000000\n",
      "mean       5.722222\n",
      "std        1.078064\n",
      "min        5.000000\n",
      "25%        5.000000\n",
      "50%        5.000000\n",
      "75%        6.000000\n",
      "max       10.000000\n",
      "Name: transferCount, dtype: float64\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 720x504 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 我们可以删掉一些异常数据，以 power 为例。  \n",
    "# 这里删不删同学可以自行判断\n",
    "# 但是要注意 test 的数据不能删 = = 不能掩耳盗铃是不是\n",
    "Train_data = outliers_proc(Train_data, 'transferCount', scale=3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import warnings\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "warnings.filterwarnings('ignore')\n",
    "%matplotlib inline\n",
    "\n",
    "import itertools\n",
    "import matplotlib.gridspec as gridspec\n",
    "from sklearn import datasets\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.naive_bayes import GaussianNB \n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "# from mlxtend.classifier import StackingClassifier\n",
    "from sklearn.model_selection import cross_val_score, train_test_split\n",
    "# from mlxtend.plotting import plot_learning_curves\n",
    "# from mlxtend.plotting import plot_decision_regions\n",
    "\n",
    "from sklearn.model_selection import StratifiedKFold\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "from sklearn import linear_model\n",
    "from sklearn import preprocessing\n",
    "from sklearn.svm import SVR\n",
    "from sklearn.decomposition import PCA,FastICA,FactorAnalysis,SparsePCA\n",
    "\n",
    "import lightgbm as lgb\n",
    "import xgboost as xgb\n",
    "from sklearn.model_selection import GridSearchCV,cross_val_score\n",
    "from sklearn.ensemble import RandomForestRegressor,GradientBoostingRegressor\n",
    "\n",
    "from sklearn.metrics import mean_squared_error, mean_absolute_error"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [],
   "source": [
    "TestA_data=Test_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "X train shape: (29874, 35)\n",
      "X test shape: (5000, 35)\n",
      "Sta of label:\n",
      "_min 0.05\n",
      "_max: 109000.0\n",
      "_mean 18.05887314052353\n",
      "_ptp 108999.95\n",
      "_std 630.7582266866006\n",
      "_var 397855.94053282496\n"
     ]
    }
   ],
   "source": [
    "numeric_features = ['transferCount', 'mileage', 'seatings','modelyear','newprice', 'v1', 'v2', 'v3', 'v4', 'v5', 'v6', 'v8', 'v9', 'v10', 'v13','v14','tradeTime_y','licenseDate_y' ]\n",
    "categorical_features = [ 'model', 'brand', 'serial', 'color', 'cityId', 'carCode', 'registerDate_d','registerDate_m','country','v11','maketype','gearbox','oiltype','tradeTime_d','tradeTime_m','licenseDate_d','licenseDate_m']\n",
    "feature_cols = numeric_features+categorical_features\n",
    "X_data = Train_data[feature_cols]\n",
    "Y_data = Train_data['price']\n",
    "X_data.replace('MISSING', -1,inplace=True)\n",
    "\n",
    "X_test  = TestA_data[feature_cols]\n",
    "X_test.replace('MISSING', -1,inplace=True)\n",
    "\n",
    "print('X train shape:',X_data.shape)\n",
    "print('X test shape:',X_test.shape)\n",
    "def Sta_inf(data):\n",
    "    print('_min',np.min(data))\n",
    "    print('_max:',np.max(data))\n",
    "    print('_mean',np.mean(data))\n",
    "    print('_ptp',np.ptp(data))\n",
    "    print('_std',np.std(data))\n",
    "    print('_var',np.var(data))\n",
    "print('Sta of label:')\n",
    "Sta_inf(Y_data)\n",
    "# X_data = X_data.fillna(-1)\n",
    "# X_test = X_test.fillna(-1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [],
   "source": [
    "def build_model_lr(x_train,y_train):\n",
    "    reg_model = linear_model.LinearRegression()\n",
    "    reg_model.fit(x_train,y_train)\n",
    "    return reg_model\n",
    "\n",
    "def build_model_ridge(x_train,y_train):\n",
    "    reg_model = linear_model.Ridge(alpha=0.8)#alphas=range(1,100,5)\n",
    "    reg_model.fit(x_train,y_train)\n",
    "    return reg_model\n",
    "\n",
    "def build_model_lasso(x_train,y_train):\n",
    "    reg_model = linear_model.LassoCV()\n",
    "    reg_model.fit(x_train,y_train)\n",
    "    return reg_model\n",
    "\n",
    "def build_model_gbdt(x_train,y_train):\n",
    "    estimator =GradientBoostingRegressor(loss='ls',subsample= 0.85,max_depth= 5,n_estimators = 100)\n",
    "    param_grid = { \n",
    "            'learning_rate': [0.05,0.08,0.1,0.2],\n",
    "            }\n",
    "    gbdt = GridSearchCV(estimator, param_grid,cv=3)\n",
    "    gbdt.fit(x_train,y_train)\n",
    "    print(gbdt.best_params_)\n",
    "    # print(gbdt.best_estimator_ )\n",
    "    return gbdt\n",
    "\n",
    "def build_model_xgb(x_train,y_train):\n",
    "    model = xgb.XGBRegressor(n_estimators=120, learning_rate=0.08, gamma=0, subsample=0.8,\\\n",
    "        colsample_bytree=0.9, max_depth=5) #, objective ='reg:squarederror'\n",
    "    model.fit(x_train, y_train)\n",
    "    return model\n",
    "\n",
    "def build_model_lgb(x_train,y_train):\n",
    "    estimator = lgb.LGBMRegressor(num_leaves=63,n_estimators = 100)\n",
    "    param_grid = {\n",
    "        'learning_rate': [0.01, 0.05, 0.1],\n",
    "    }\n",
    "    gbm = GridSearchCV(estimator, param_grid)\n",
    "    gbm.fit(x_train, y_train)\n",
    "    return gbm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [],
   "source": [
    "# ## xgb\n",
    "# xgr = xgb.XGBRegressor(n_estimators=120, learning_rate=0.1, subsample=0.8,\\\n",
    "#         colsample_bytree=0.9, max_depth=7) # ,objective ='reg:squarederror'\n",
    "\n",
    "# scores_train = []\n",
    "# scores = []\n",
    "\n",
    "# ## 5折交叉验证方式\n",
    "# sk=StratifiedKFold(n_splits=5,shuffle=True,random_state=0)\n",
    "# for train_ind,val_ind in sk.split(X_data,Y_data):\n",
    "    \n",
    "#     train_x=X_data.iloc[train_ind].values\n",
    "#     train_y=Y_data.iloc[train_ind]\n",
    "#     val_x=X_data.iloc[val_ind].values\n",
    "#     val_y=Y_data.iloc[val_ind]\n",
    "    \n",
    "#     xgr.fit(train_x,train_y)\n",
    "#     pred_train_xgb=xgr.predict(train_x)\n",
    "#     pred_xgb=xgr.predict(val_x)\n",
    "    \n",
    "#     score_train = mean_absolute_error(train_y,pred_train_xgb)\n",
    "#     scores_train.append(score_train)\n",
    "#     score = mean_absolute_error(val_y,pred_xgb)\n",
    "#     scores.append(score)\n",
    "\n",
    "# print('Train mae:',np.mean(score_train))\n",
    "# print('Val mae',np.mean(scores))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {},
   "outputs": [],
   "source": [
    "## Split data with val\n",
    "x_train,x_val,y_train,y_val = train_test_split(X_data,Y_data,test_size=0.3)\n",
    "\n",
    "# Train and Predict\n",
    "# print('Predict LR...')\n",
    "# model_lr = build_model_lr(x_train,y_train)\n",
    "# val_lr = model_lr.predict(x_val)\n",
    "# subA_lr = model_lr.predict(X_test)\n",
    "\n",
    "# print('Predict Ridge...')\n",
    "# model_ridge = build_model_ridge(x_train,y_train)\n",
    "# val_ridge = model_ridge.predict(x_val)\n",
    "# subA_ridge = model_ridge.predict(X_test)\n",
    "\n",
    "# print('Predict Lasso...')\n",
    "# model_lasso = build_model_lasso(x_train,y_train)\n",
    "# val_lasso = model_lasso.predict(x_val)\n",
    "# subA_lasso = model_lasso.predict(X_test)\n",
    "\n",
    "# print('Predict GBDT...')\n",
    "# model_gbdt = build_model_gbdt(x_train,y_train)\n",
    "# val_gbdt = model_gbdt.predict(x_val)\n",
    "# subA_gbdt = model_gbdt.predict(X_test)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {},
   "outputs": [],
   "source": [
    "# from xgboost import XGBRegressor\n",
    "# rf = XGBRegressor(n_estimators = 2500 , random_state = 0 , max_depth = 27)\n",
    "# rf.fit(x_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Carat Weight</th>\n",
       "      <th>Cut</th>\n",
       "      <th>Color</th>\n",
       "      <th>Clarity</th>\n",
       "      <th>Polish</th>\n",
       "      <th>Symmetry</th>\n",
       "      <th>Report</th>\n",
       "      <th>Price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.10</td>\n",
       "      <td>Ideal</td>\n",
       "      <td>H</td>\n",
       "      <td>SI1</td>\n",
       "      <td>VG</td>\n",
       "      <td>EX</td>\n",
       "      <td>GIA</td>\n",
       "      <td>5169</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.83</td>\n",
       "      <td>Ideal</td>\n",
       "      <td>H</td>\n",
       "      <td>VS1</td>\n",
       "      <td>ID</td>\n",
       "      <td>ID</td>\n",
       "      <td>AGSL</td>\n",
       "      <td>3470</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.85</td>\n",
       "      <td>Ideal</td>\n",
       "      <td>H</td>\n",
       "      <td>SI1</td>\n",
       "      <td>EX</td>\n",
       "      <td>EX</td>\n",
       "      <td>GIA</td>\n",
       "      <td>3183</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.91</td>\n",
       "      <td>Ideal</td>\n",
       "      <td>E</td>\n",
       "      <td>SI1</td>\n",
       "      <td>VG</td>\n",
       "      <td>VG</td>\n",
       "      <td>GIA</td>\n",
       "      <td>4370</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.83</td>\n",
       "      <td>Ideal</td>\n",
       "      <td>G</td>\n",
       "      <td>SI1</td>\n",
       "      <td>EX</td>\n",
       "      <td>EX</td>\n",
       "      <td>GIA</td>\n",
       "      <td>3171</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Carat Weight    Cut Color Clarity Polish Symmetry Report  Price\n",
       "0          1.10  Ideal     H     SI1     VG       EX    GIA   5169\n",
       "1          0.83  Ideal     H     VS1     ID       ID   AGSL   3470\n",
       "2          0.85  Ideal     H     SI1     EX       EX    GIA   3183\n",
       "3          0.91  Ideal     E     SI1     VG       VG    GIA   4370\n",
       "4          0.83  Ideal     G     SI1     EX       EX    GIA   3171"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from pycaret.datasets import get_data\n",
    "dataset = get_data('diamond')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Data for Modeling: (5400, 8)\n",
      "Unseen Data For Predictions: (600, 8)\n"
     ]
    }
   ],
   "source": [
    "# x_train.fillna(-1)\n",
    "data = dataset.sample(frac=0.9, random_state=786).reset_index(drop=True)\n",
    "data_unseen = dataset.drop(data.index).reset_index(drop=True)\n",
    "\n",
    "print('Data for Modeling: ' + str(data.shape))\n",
    "print('Unseen Data For Predictions: ' + str(data_unseen.shape))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style  type=\"text/css\" >\n",
       "#T_a4eb2_row3_col1{\n",
       "            background-color:  lightgreen;\n",
       "        }</style><table id=\"T_a4eb2_\" ><thead>    <tr>        <th class=\"blank level0\" ></th>        <th class=\"col_heading level0 col0\" >Description</th>        <th class=\"col_heading level0 col1\" >Value</th>    </tr></thead><tbody>\n",
       "                <tr>\n",
       "                        <th id=\"T_a4eb2_level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
       "                        <td id=\"T_a4eb2_row0_col0\" class=\"data row0 col0\" >session_id</td>\n",
       "                        <td id=\"T_a4eb2_row0_col1\" class=\"data row0 col1\" >123</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_a4eb2_level0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
       "                        <td id=\"T_a4eb2_row1_col0\" class=\"data row1 col0\" >Target</td>\n",
       "                        <td id=\"T_a4eb2_row1_col1\" class=\"data row1 col1\" >price</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_a4eb2_level0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
       "                        <td id=\"T_a4eb2_row2_col0\" class=\"data row2 col0\" >Original Data</td>\n",
       "                        <td id=\"T_a4eb2_row2_col1\" class=\"data row2 col1\" >(29999, 39)</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_a4eb2_level0_row3\" class=\"row_heading level0 row3\" >3</th>\n",
       "                        <td id=\"T_a4eb2_row3_col0\" class=\"data row3 col0\" >Missing Values</td>\n",
       "                        <td id=\"T_a4eb2_row3_col1\" class=\"data row3 col1\" >True</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_a4eb2_level0_row4\" class=\"row_heading level0 row4\" >4</th>\n",
       "                        <td id=\"T_a4eb2_row4_col0\" class=\"data row4 col0\" >Numeric Features</td>\n",
       "                        <td id=\"T_a4eb2_row4_col1\" class=\"data row4 col1\" >15</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_a4eb2_level0_row5\" class=\"row_heading level0 row5\" >5</th>\n",
       "                        <td id=\"T_a4eb2_row5_col0\" class=\"data row5 col0\" >Categorical Features</td>\n",
       "                        <td id=\"T_a4eb2_row5_col1\" class=\"data row5 col1\" >18</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_a4eb2_level0_row6\" class=\"row_heading level0 row6\" >6</th>\n",
       "                        <td id=\"T_a4eb2_row6_col0\" class=\"data row6 col0\" >Ordinal Features</td>\n",
       "                        <td id=\"T_a4eb2_row6_col1\" class=\"data row6 col1\" >False</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_a4eb2_level0_row7\" class=\"row_heading level0 row7\" >7</th>\n",
       "                        <td id=\"T_a4eb2_row7_col0\" class=\"data row7 col0\" >High Cardinality Features</td>\n",
       "                        <td id=\"T_a4eb2_row7_col1\" class=\"data row7 col1\" >False</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_a4eb2_level0_row8\" class=\"row_heading level0 row8\" >8</th>\n",
       "                        <td id=\"T_a4eb2_row8_col0\" class=\"data row8 col0\" >High Cardinality Method</td>\n",
       "                        <td id=\"T_a4eb2_row8_col1\" class=\"data row8 col1\" >None</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_a4eb2_level0_row9\" class=\"row_heading level0 row9\" >9</th>\n",
       "                        <td id=\"T_a4eb2_row9_col0\" class=\"data row9 col0\" >Transformed Train Set</td>\n",
       "                        <td id=\"T_a4eb2_row9_col1\" class=\"data row9 col1\" >(20999, 2262)</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_a4eb2_level0_row10\" class=\"row_heading level0 row10\" >10</th>\n",
       "                        <td id=\"T_a4eb2_row10_col0\" class=\"data row10 col0\" >Transformed Test Set</td>\n",
       "                        <td id=\"T_a4eb2_row10_col1\" class=\"data row10 col1\" >(9000, 2262)</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_a4eb2_level0_row11\" class=\"row_heading level0 row11\" >11</th>\n",
       "                        <td id=\"T_a4eb2_row11_col0\" class=\"data row11 col0\" >Shuffle Train-Test</td>\n",
       "                        <td id=\"T_a4eb2_row11_col1\" class=\"data row11 col1\" >True</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_a4eb2_level0_row12\" class=\"row_heading level0 row12\" >12</th>\n",
       "                        <td id=\"T_a4eb2_row12_col0\" class=\"data row12 col0\" >Stratify Train-Test</td>\n",
       "                        <td id=\"T_a4eb2_row12_col1\" class=\"data row12 col1\" >False</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_a4eb2_level0_row13\" class=\"row_heading level0 row13\" >13</th>\n",
       "                        <td id=\"T_a4eb2_row13_col0\" class=\"data row13 col0\" >Fold Generator</td>\n",
       "                        <td id=\"T_a4eb2_row13_col1\" class=\"data row13 col1\" >KFold</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_a4eb2_level0_row14\" class=\"row_heading level0 row14\" >14</th>\n",
       "                        <td id=\"T_a4eb2_row14_col0\" class=\"data row14 col0\" >Fold Number</td>\n",
       "                        <td id=\"T_a4eb2_row14_col1\" class=\"data row14 col1\" >10</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_a4eb2_level0_row15\" class=\"row_heading level0 row15\" >15</th>\n",
       "                        <td id=\"T_a4eb2_row15_col0\" class=\"data row15 col0\" >CPU Jobs</td>\n",
       "                        <td id=\"T_a4eb2_row15_col1\" class=\"data row15 col1\" >-1</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_a4eb2_level0_row16\" class=\"row_heading level0 row16\" >16</th>\n",
       "                        <td id=\"T_a4eb2_row16_col0\" class=\"data row16 col0\" >Use GPU</td>\n",
       "                        <td id=\"T_a4eb2_row16_col1\" class=\"data row16 col1\" >False</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_a4eb2_level0_row17\" class=\"row_heading level0 row17\" >17</th>\n",
       "                        <td id=\"T_a4eb2_row17_col0\" class=\"data row17 col0\" >Log Experiment</td>\n",
       "                        <td id=\"T_a4eb2_row17_col1\" class=\"data row17 col1\" >False</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_a4eb2_level0_row18\" class=\"row_heading level0 row18\" >18</th>\n",
       "                        <td id=\"T_a4eb2_row18_col0\" class=\"data row18 col0\" >Experiment Name</td>\n",
       "                        <td id=\"T_a4eb2_row18_col1\" class=\"data row18 col1\" >reg-default-name</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_a4eb2_level0_row19\" class=\"row_heading level0 row19\" >19</th>\n",
       "                        <td id=\"T_a4eb2_row19_col0\" class=\"data row19 col0\" >USI</td>\n",
       "                        <td id=\"T_a4eb2_row19_col1\" class=\"data row19 col1\" >7c4f</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_a4eb2_level0_row20\" class=\"row_heading level0 row20\" >20</th>\n",
       "                        <td id=\"T_a4eb2_row20_col0\" class=\"data row20 col0\" >Imputation Type</td>\n",
       "                        <td id=\"T_a4eb2_row20_col1\" class=\"data row20 col1\" >simple</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_a4eb2_level0_row21\" class=\"row_heading level0 row21\" >21</th>\n",
       "                        <td id=\"T_a4eb2_row21_col0\" class=\"data row21 col0\" >Iterative Imputation Iteration</td>\n",
       "                        <td id=\"T_a4eb2_row21_col1\" class=\"data row21 col1\" >None</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_a4eb2_level0_row22\" class=\"row_heading level0 row22\" >22</th>\n",
       "                        <td id=\"T_a4eb2_row22_col0\" class=\"data row22 col0\" >Numeric Imputer</td>\n",
       "                        <td id=\"T_a4eb2_row22_col1\" class=\"data row22 col1\" >mean</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_a4eb2_level0_row23\" class=\"row_heading level0 row23\" >23</th>\n",
       "                        <td id=\"T_a4eb2_row23_col0\" class=\"data row23 col0\" >Iterative Imputation Numeric Model</td>\n",
       "                        <td id=\"T_a4eb2_row23_col1\" class=\"data row23 col1\" >None</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_a4eb2_level0_row24\" class=\"row_heading level0 row24\" >24</th>\n",
       "                        <td id=\"T_a4eb2_row24_col0\" class=\"data row24 col0\" >Categorical Imputer</td>\n",
       "                        <td id=\"T_a4eb2_row24_col1\" class=\"data row24 col1\" >constant</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_a4eb2_level0_row25\" class=\"row_heading level0 row25\" >25</th>\n",
       "                        <td id=\"T_a4eb2_row25_col0\" class=\"data row25 col0\" >Iterative Imputation Categorical Model</td>\n",
       "                        <td id=\"T_a4eb2_row25_col1\" class=\"data row25 col1\" >None</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_a4eb2_level0_row26\" class=\"row_heading level0 row26\" >26</th>\n",
       "                        <td id=\"T_a4eb2_row26_col0\" class=\"data row26 col0\" >Unknown Categoricals Handling</td>\n",
       "                        <td id=\"T_a4eb2_row26_col1\" class=\"data row26 col1\" >least_frequent</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_a4eb2_level0_row27\" class=\"row_heading level0 row27\" >27</th>\n",
       "                        <td id=\"T_a4eb2_row27_col0\" class=\"data row27 col0\" >Normalize</td>\n",
       "                        <td id=\"T_a4eb2_row27_col1\" class=\"data row27 col1\" >False</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_a4eb2_level0_row28\" class=\"row_heading level0 row28\" >28</th>\n",
       "                        <td id=\"T_a4eb2_row28_col0\" class=\"data row28 col0\" >Normalize Method</td>\n",
       "                        <td id=\"T_a4eb2_row28_col1\" class=\"data row28 col1\" >None</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_a4eb2_level0_row29\" class=\"row_heading level0 row29\" >29</th>\n",
       "                        <td id=\"T_a4eb2_row29_col0\" class=\"data row29 col0\" >Transformation</td>\n",
       "                        <td id=\"T_a4eb2_row29_col1\" class=\"data row29 col1\" >False</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_a4eb2_level0_row30\" class=\"row_heading level0 row30\" >30</th>\n",
       "                        <td id=\"T_a4eb2_row30_col0\" class=\"data row30 col0\" >Transformation Method</td>\n",
       "                        <td id=\"T_a4eb2_row30_col1\" class=\"data row30 col1\" >None</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_a4eb2_level0_row31\" class=\"row_heading level0 row31\" >31</th>\n",
       "                        <td id=\"T_a4eb2_row31_col0\" class=\"data row31 col0\" >PCA</td>\n",
       "                        <td id=\"T_a4eb2_row31_col1\" class=\"data row31 col1\" >False</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_a4eb2_level0_row32\" class=\"row_heading level0 row32\" >32</th>\n",
       "                        <td id=\"T_a4eb2_row32_col0\" class=\"data row32 col0\" >PCA Method</td>\n",
       "                        <td id=\"T_a4eb2_row32_col1\" class=\"data row32 col1\" >None</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_a4eb2_level0_row33\" class=\"row_heading level0 row33\" >33</th>\n",
       "                        <td id=\"T_a4eb2_row33_col0\" class=\"data row33 col0\" >PCA Components</td>\n",
       "                        <td id=\"T_a4eb2_row33_col1\" class=\"data row33 col1\" >None</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_a4eb2_level0_row34\" class=\"row_heading level0 row34\" >34</th>\n",
       "                        <td id=\"T_a4eb2_row34_col0\" class=\"data row34 col0\" >Ignore Low Variance</td>\n",
       "                        <td id=\"T_a4eb2_row34_col1\" class=\"data row34 col1\" >False</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_a4eb2_level0_row35\" class=\"row_heading level0 row35\" >35</th>\n",
       "                        <td id=\"T_a4eb2_row35_col0\" class=\"data row35 col0\" >Combine Rare Levels</td>\n",
       "                        <td id=\"T_a4eb2_row35_col1\" class=\"data row35 col1\" >False</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_a4eb2_level0_row36\" class=\"row_heading level0 row36\" >36</th>\n",
       "                        <td id=\"T_a4eb2_row36_col0\" class=\"data row36 col0\" >Rare Level Threshold</td>\n",
       "                        <td id=\"T_a4eb2_row36_col1\" class=\"data row36 col1\" >None</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_a4eb2_level0_row37\" class=\"row_heading level0 row37\" >37</th>\n",
       "                        <td id=\"T_a4eb2_row37_col0\" class=\"data row37 col0\" >Numeric Binning</td>\n",
       "                        <td id=\"T_a4eb2_row37_col1\" class=\"data row37 col1\" >False</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_a4eb2_level0_row38\" class=\"row_heading level0 row38\" >38</th>\n",
       "                        <td id=\"T_a4eb2_row38_col0\" class=\"data row38 col0\" >Remove Outliers</td>\n",
       "                        <td id=\"T_a4eb2_row38_col1\" class=\"data row38 col1\" >False</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_a4eb2_level0_row39\" class=\"row_heading level0 row39\" >39</th>\n",
       "                        <td id=\"T_a4eb2_row39_col0\" class=\"data row39 col0\" >Outliers Threshold</td>\n",
       "                        <td id=\"T_a4eb2_row39_col1\" class=\"data row39 col1\" >None</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_a4eb2_level0_row40\" class=\"row_heading level0 row40\" >40</th>\n",
       "                        <td id=\"T_a4eb2_row40_col0\" class=\"data row40 col0\" >Remove Multicollinearity</td>\n",
       "                        <td id=\"T_a4eb2_row40_col1\" class=\"data row40 col1\" >False</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_a4eb2_level0_row41\" class=\"row_heading level0 row41\" >41</th>\n",
       "                        <td id=\"T_a4eb2_row41_col0\" class=\"data row41 col0\" >Multicollinearity Threshold</td>\n",
       "                        <td id=\"T_a4eb2_row41_col1\" class=\"data row41 col1\" >None</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_a4eb2_level0_row42\" class=\"row_heading level0 row42\" >42</th>\n",
       "                        <td id=\"T_a4eb2_row42_col0\" class=\"data row42 col0\" >Clustering</td>\n",
       "                        <td id=\"T_a4eb2_row42_col1\" class=\"data row42 col1\" >False</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_a4eb2_level0_row43\" class=\"row_heading level0 row43\" >43</th>\n",
       "                        <td id=\"T_a4eb2_row43_col0\" class=\"data row43 col0\" >Clustering Iteration</td>\n",
       "                        <td id=\"T_a4eb2_row43_col1\" class=\"data row43 col1\" >None</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_a4eb2_level0_row44\" class=\"row_heading level0 row44\" >44</th>\n",
       "                        <td id=\"T_a4eb2_row44_col0\" class=\"data row44 col0\" >Polynomial Features</td>\n",
       "                        <td id=\"T_a4eb2_row44_col1\" class=\"data row44 col1\" >False</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_a4eb2_level0_row45\" class=\"row_heading level0 row45\" >45</th>\n",
       "                        <td id=\"T_a4eb2_row45_col0\" class=\"data row45 col0\" >Polynomial Degree</td>\n",
       "                        <td id=\"T_a4eb2_row45_col1\" class=\"data row45 col1\" >None</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_a4eb2_level0_row46\" class=\"row_heading level0 row46\" >46</th>\n",
       "                        <td id=\"T_a4eb2_row46_col0\" class=\"data row46 col0\" >Trignometry Features</td>\n",
       "                        <td id=\"T_a4eb2_row46_col1\" class=\"data row46 col1\" >False</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_a4eb2_level0_row47\" class=\"row_heading level0 row47\" >47</th>\n",
       "                        <td id=\"T_a4eb2_row47_col0\" class=\"data row47 col0\" >Polynomial Threshold</td>\n",
       "                        <td id=\"T_a4eb2_row47_col1\" class=\"data row47 col1\" >None</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_a4eb2_level0_row48\" class=\"row_heading level0 row48\" >48</th>\n",
       "                        <td id=\"T_a4eb2_row48_col0\" class=\"data row48 col0\" >Group Features</td>\n",
       "                        <td id=\"T_a4eb2_row48_col1\" class=\"data row48 col1\" >False</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_a4eb2_level0_row49\" class=\"row_heading level0 row49\" >49</th>\n",
       "                        <td id=\"T_a4eb2_row49_col0\" class=\"data row49 col0\" >Feature Selection</td>\n",
       "                        <td id=\"T_a4eb2_row49_col1\" class=\"data row49 col1\" >False</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_a4eb2_level0_row50\" class=\"row_heading level0 row50\" >50</th>\n",
       "                        <td id=\"T_a4eb2_row50_col0\" class=\"data row50 col0\" >Features Selection Threshold</td>\n",
       "                        <td id=\"T_a4eb2_row50_col1\" class=\"data row50 col1\" >None</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_a4eb2_level0_row51\" class=\"row_heading level0 row51\" >51</th>\n",
       "                        <td id=\"T_a4eb2_row51_col0\" class=\"data row51 col0\" >Feature Interaction</td>\n",
       "                        <td id=\"T_a4eb2_row51_col1\" class=\"data row51 col1\" >False</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_a4eb2_level0_row52\" class=\"row_heading level0 row52\" >52</th>\n",
       "                        <td id=\"T_a4eb2_row52_col0\" class=\"data row52 col0\" >Feature Ratio</td>\n",
       "                        <td id=\"T_a4eb2_row52_col1\" class=\"data row52 col1\" >False</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_a4eb2_level0_row53\" class=\"row_heading level0 row53\" >53</th>\n",
       "                        <td id=\"T_a4eb2_row53_col0\" class=\"data row53 col0\" >Interaction Threshold</td>\n",
       "                        <td id=\"T_a4eb2_row53_col1\" class=\"data row53 col1\" >None</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_a4eb2_level0_row54\" class=\"row_heading level0 row54\" >54</th>\n",
       "                        <td id=\"T_a4eb2_row54_col0\" class=\"data row54 col0\" >Transform Target</td>\n",
       "                        <td id=\"T_a4eb2_row54_col1\" class=\"data row54 col1\" >False</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_a4eb2_level0_row55\" class=\"row_heading level0 row55\" >55</th>\n",
       "                        <td id=\"T_a4eb2_row55_col0\" class=\"data row55 col0\" >Transform Target Method</td>\n",
       "                        <td id=\"T_a4eb2_row55_col1\" class=\"data row55 col1\" >box-cox</td>\n",
       "            </tr>\n",
       "    </tbody></table>"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x23f5f7a1d00>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from pycaret.regression import *\n",
    "exp_reg101 = setup(data = Train_data, target = 'price', session_id=123) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "766116eded064c0f91f0c988c58be8d5",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "IntProgress(value=0, description='Processing: ', max=99)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
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       "      <th></th>\n",
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       "      <th></th>\n",
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       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Initiated</th>\n",
       "      <td>. . . . . . . . . . . . . . . . . .</td>\n",
       "      <td>20:08:24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Status</th>\n",
       "      <td>. . . . . . . . . . . . . . . . . .</td>\n",
       "      <td>Fitting 10 Folds</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Estimator</th>\n",
       "      <td>. . . . . . . . . . . . . . . . . .</td>\n",
       "      <td>Lasso Least Angle Regression</td>\n",
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      ],
      "text/plain": [
       "                                                                            \n",
       "                                                                            \n",
       "Initiated  . . . . . . . . . . . . . . . . . .                      20:08:24\n",
       "Status     . . . . . . . . . . . . . . . . . .              Fitting 10 Folds\n",
       "Estimator  . . . . . . . . . . . . . . . . . .  Lasso Least Angle Regression"
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       "    }#T_397de_row0_col0,#T_397de_row0_col1,#T_397de_row0_col2,#T_397de_row0_col3,#T_397de_row0_col4,#T_397de_row0_col5,#T_397de_row0_col6,#T_397de_row0_col7,#T_397de_row1_col0,#T_397de_row1_col1,#T_397de_row1_col2,#T_397de_row1_col3,#T_397de_row1_col4,#T_397de_row1_col5,#T_397de_row1_col6,#T_397de_row1_col7,#T_397de_row2_col0,#T_397de_row2_col1,#T_397de_row2_col2,#T_397de_row2_col3,#T_397de_row2_col4,#T_397de_row2_col5,#T_397de_row2_col6,#T_397de_row2_col7,#T_397de_row3_col0,#T_397de_row3_col1,#T_397de_row3_col2,#T_397de_row3_col3,#T_397de_row3_col4,#T_397de_row3_col5,#T_397de_row3_col6,#T_397de_row3_col7,#T_397de_row4_col0,#T_397de_row4_col1,#T_397de_row4_col2,#T_397de_row4_col3,#T_397de_row4_col4,#T_397de_row4_col5,#T_397de_row4_col6,#T_397de_row4_col7{\n",
       "            text-align:  left;\n",
       "        }</style><table id=\"T_397de_\" ><thead>    <tr>        <th class=\"blank level0\" ></th>        <th class=\"col_heading level0 col0\" >Model</th>        <th class=\"col_heading level0 col1\" >MAE</th>        <th class=\"col_heading level0 col2\" >MSE</th>        <th class=\"col_heading level0 col3\" >RMSE</th>        <th class=\"col_heading level0 col4\" >R2</th>        <th class=\"col_heading level0 col5\" >RMSLE</th>        <th class=\"col_heading level0 col6\" >MAPE</th>        <th class=\"col_heading level0 col7\" >TT (Sec)</th>    </tr></thead><tbody>\n",
       "                <tr>\n",
       "                        <th id=\"T_397de_level0_row0\" class=\"row_heading level0 row0\" >ridge</th>\n",
       "                        <td id=\"T_397de_row0_col0\" class=\"data row0 col0\" >Ridge Regression</td>\n",
       "                        <td id=\"T_397de_row0_col1\" class=\"data row0 col1\" >2.0275</td>\n",
       "                        <td id=\"T_397de_row0_col2\" class=\"data row0 col2\" >33.8999</td>\n",
       "                        <td id=\"T_397de_row0_col3\" class=\"data row0 col3\" >5.4838</td>\n",
       "                        <td id=\"T_397de_row0_col4\" class=\"data row0 col4\" >0.8786</td>\n",
       "                        <td id=\"T_397de_row0_col5\" class=\"data row0 col5\" >0.2576</td>\n",
       "                        <td id=\"T_397de_row0_col6\" class=\"data row0 col6\" >0.2524</td>\n",
       "                        <td id=\"T_397de_row0_col7\" class=\"data row0 col7\" >15.7500</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_397de_level0_row1\" class=\"row_heading level0 row1\" >lar</th>\n",
       "                        <td id=\"T_397de_row1_col0\" class=\"data row1 col0\" >Least Angle Regression</td>\n",
       "                        <td id=\"T_397de_row1_col1\" class=\"data row1 col1\" >2.3519</td>\n",
       "                        <td id=\"T_397de_row1_col2\" class=\"data row1 col2\" >41.0159</td>\n",
       "                        <td id=\"T_397de_row1_col3\" class=\"data row1 col3\" >5.9583</td>\n",
       "                        <td id=\"T_397de_row1_col4\" class=\"data row1 col4\" >0.8569</td>\n",
       "                        <td id=\"T_397de_row1_col5\" class=\"data row1 col5\" >0.2710</td>\n",
       "                        <td id=\"T_397de_row1_col6\" class=\"data row1 col6\" >0.2871</td>\n",
       "                        <td id=\"T_397de_row1_col7\" class=\"data row1 col7\" >16.8940</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_397de_level0_row2\" class=\"row_heading level0 row2\" >lr</th>\n",
       "                        <td id=\"T_397de_row2_col0\" class=\"data row2 col0\" >Linear Regression</td>\n",
       "                        <td id=\"T_397de_row2_col1\" class=\"data row2 col1\" >3.7460</td>\n",
       "                        <td id=\"T_397de_row2_col2\" class=\"data row2 col2\" >54.6331</td>\n",
       "                        <td id=\"T_397de_row2_col3\" class=\"data row2 col3\" >7.0821</td>\n",
       "                        <td id=\"T_397de_row2_col4\" class=\"data row2 col4\" >0.7964</td>\n",
       "                        <td id=\"T_397de_row2_col5\" class=\"data row2 col5\" >0.4664</td>\n",
       "                        <td id=\"T_397de_row2_col6\" class=\"data row2 col6\" >0.5138</td>\n",
       "                        <td id=\"T_397de_row2_col7\" class=\"data row2 col7\" >10.0070</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_397de_level0_row3\" class=\"row_heading level0 row3\" >en</th>\n",
       "                        <td id=\"T_397de_row3_col0\" class=\"data row3 col0\" >Elastic Net</td>\n",
       "                        <td id=\"T_397de_row3_col1\" class=\"data row3 col1\" >4.0932</td>\n",
       "                        <td id=\"T_397de_row3_col2\" class=\"data row3 col2\" >70.1590</td>\n",
       "                        <td id=\"T_397de_row3_col3\" class=\"data row3 col3\" >8.2518</td>\n",
       "                        <td id=\"T_397de_row3_col4\" class=\"data row3 col4\" >0.7261</td>\n",
       "                        <td id=\"T_397de_row3_col5\" class=\"data row3 col5\" >0.4238</td>\n",
       "                        <td id=\"T_397de_row3_col6\" class=\"data row3 col6\" >0.4933</td>\n",
       "                        <td id=\"T_397de_row3_col7\" class=\"data row3 col7\" >0.7650</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_397de_level0_row4\" class=\"row_heading level0 row4\" >lasso</th>\n",
       "                        <td id=\"T_397de_row4_col0\" class=\"data row4 col0\" >Lasso Regression</td>\n",
       "                        <td id=\"T_397de_row4_col1\" class=\"data row4 col1\" >4.0876</td>\n",
       "                        <td id=\"T_397de_row4_col2\" class=\"data row4 col2\" >70.2305</td>\n",
       "                        <td id=\"T_397de_row4_col3\" class=\"data row4 col3\" >8.2556</td>\n",
       "                        <td id=\"T_397de_row4_col4\" class=\"data row4 col4\" >0.7259</td>\n",
       "                        <td id=\"T_397de_row4_col5\" class=\"data row4 col5\" >0.4233</td>\n",
       "                        <td id=\"T_397de_row4_col6\" class=\"data row4 col6\" >0.4919</td>\n",
       "                        <td id=\"T_397de_row4_col7\" class=\"data row4 col7\" >1.6220</td>\n",
       "            </tr>\n",
       "    </tbody></table>"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x23f44346af0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "compare_models()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'create_model' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-1-ca4f8233e26c>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mlightgbm\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcreate_model\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'ridge'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m: name 'create_model' is not defined"
     ]
    }
   ],
   "source": [
    "ridge = create_model('ridge')\n",
    "tuned_ada = tune_model('ada')\n",
    "predict_model(ada)\n",
    "final_ada = finalize_model(ada)\n",
    "unseen_predictions = predict_model(final_ada, data=data_unseen)\n",
    "unseen_predictions.head()\n",
    "save_model(final_ada,'Final Lightgbm Model 08Feb2020')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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